Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence, July 27 -31, 2014, Québec City, Québec, Canada. AAAI Press 【DBLP Link】
【Paper Link】 【Pages】:2-8
【Authors】: Yang Bao ; Hui Fang ; Jie Zhang
【Abstract】: Although users' preference is semantically reflected in the free-form review texts, this wealth of information was not fully exploited for learning recommender models. Specifically, almost all existing recommendation algorithms only exploit rating scores in order to find users' preference, but ignore the review texts accompanied with rating information. In this paper, we propose a novel matrix factorization model (called TopicMF) which simultaneously considers the ratings and accompanied review texts. Experimental results on 22 real-world datasets show the superiority of our model over the state-of-the-art models, demonstrating its effectiveness for recommendation tasks.
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【Paper Link】 【Pages】:9-15
【Authors】: Chaochao Chen ; Xiaolin Zheng ; Yan Wang ; Fuxing Hong ; Zhen Lin
【Abstract】: Online social networking sites have become popular platforms on which users can link with each other and share information, not only basic rating information but also information such as contexts, social relationships, and item contents. However, as far as we know, no existing works systematically combine diverse types of information to build more accurate recommender systems. In this paper, we propose a novel context-aware hierarchical Bayesian method. First, we propose the use of spectral clustering for user-item subgrouping, so that users and items in similar contexts are grouped. We then propose a novel hierarchical Bayesian model that can make predictions for each user-item subgroup, our model incorporate not only topic modeling to mine item content but also social matrix factorization to handle ratings and social relationships. Experiments on an Epinions dataset show that our method significantly improves recommendation performance compared with six categories of state-of-the-art recommendation methods in terms of both prediction accuracy and recall. We have also conducted experiments to study the extent to which ratings, contexts, social relationships, and item contents contribute to recommendation performance in terms of prediction accuracy and recall.
【Keywords】: recommender systems; matrix factorization; social network; context-aware
【Paper Link】 【Pages】:16-22
【Authors】: Yueguo Chen ; Lexi Gao ; Shuming Shi ; Xiaoyong Du ; Ji-Rong Wen
【Abstract】: Entity search is to retrieve a ranked list of named entities of target types to a given query. In this paper, we propose an approach of entity search by formalizing both context matching and category matching. In addition, we propose a result re-ranking strategy that can be easily adapted to achieve a hybrid of two context matching strategies. Experiments on the INEX 2009 entity ranking task show that the proposed approach achieves a significant improvement of the entity search performance (xinfAP from 0.27 to 0.39) over the existing solutions.
【Keywords】: entity search; language model
【Paper Link】 【Pages】:23-29
【Authors】: Lei Cui ; Ming Zhou ; Qiming Chen ; Dongdong Zhang ; Mu Li
【Abstract】: Contemporary machine translation systems usually rely on offline data retrieved from the web for individual model training, such as translation models and language models. In contrast to existing methods, we propose a novel approach that treats machine translation as a web search task and utilizes the web on the fly to acquire translation knowledge. This end-to-end approach takes advantage of fresh web search results that are capable of leveraging tremendous web knowledge to obtain phrase-level candidates on demand and then compose sentence-level translations. Experimental results show that our web-based machine translation method demonstrates very promising performance in leveraging fresh translation knowledge and making translation decisions. Furthermore, when combined with offline models, it significantly outperforms a state-of-the-art phrase-based statistical machine translation system.
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【Paper Link】 【Pages】:30-36
【Authors】: Hui Fang ; Yang Bao ; Jie Zhang
【Abstract】: Trust has been used to replace or complement rating-based similarity in recommender systems, to improve the accuracy of rating prediction. However, people trusting each other may not always share similar preferences. In this paper, we try to fill in this gap by decomposing the original single-aspect trust information into four general trust aspects, i.e. benevolence, integrity, competence, and predictability, and further employing the support vector regression technique to incorporate them into the probabilistic matrix factorization model for rating prediction in recommender systems. Experimental results on four datasets demonstrate the superiority of our method over the state-of-the-art approaches.
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【Paper Link】 【Pages】:37-43
【Authors】: Shanshan Feng ; Xuefeng Chen ; Gao Cong ; Yifeng Zeng ; Yeow Meng Chee ; Yanping Xiang
【Abstract】: Influence maximization problem is to find a set of seed nodes in a social network such that their influence spread is maximized under certain propagation models. A few algorithms have been proposed for solving this problem. However, they have not considered the impact of novelty decay on influence propagation, i.e., repeated exposures will have diminishing influence on users. In this paper, we consider the problem of influence maximization with novelty decay (IMND). We investigate the effect of novelty decay on influence propagation on real-life datasets and formulate the IMND problem. We further analyze the problem properties and propose an influence estimation technique. We demonstrate the performance of our algorithms on four social networks.
【Keywords】: social networks; influence maximization; novelty decay
【Paper Link】 【Pages】:44-50
【Authors】: Christos Giatsidis ; Fragkiskos D. Malliaros ; Dimitrios M. Thilikos ; Michalis Vazirgiannis
【Abstract】: Graph clustering or community detection constitutes an important task forinvestigating the internal structure of graphs, with a plethora of applications in several domains. Traditional tools for graph clustering, such asspectral methods, typically suffer from high time and space complexity. In thisarticle, we present CoreCluster, an efficient graph clusteringframework based on the concept of graph degeneracy, that can be used along withany known graph clustering algorithm. Our approach capitalizes on processing thegraph in a hierarchical manner provided by its core expansion sequence, anordered partition of the graph into different levels according to the k-coredecomposition. Such a partition provides a way to process the graph inan incremental manner that preserves its clustering structure, whilemaking the execution of the chosen clustering algorithm much faster due to thesmaller size of the graph's partitions onto which the algorithm operates.
【Keywords】: Graph clustering; Community detection; Graph degeneracy; Graph mining
【Paper Link】 【Pages】:51-58
【Authors】: Md. Kamrul Hasan ; Christopher Joseph Pal
【Abstract】: We present a series of visual information extraction experiments using the Faces of Wikipedia database - a new resource that we release into the public domain for both recognition and extraction research containing over 50,000 identities and 60,000 disambiguated images of faces. We compare different techniques for automatically extracting the faces corresponding to the subject of a Wikipedia biography within the images appearing on the page. Our top performing approach is based on probabilistic graphical models and uses the text of Wikipedia pages, similarities of faces as well as various other features of the document, meta-data and image files. Our method resolves the problem jointly for all detected faces on a page. While our experiments focus on extracting faces from Wikipedia biographies, our approach is easily adapted to other types of documents and multiple documents. We focus on Wikipedia because the content is a Creative Commons resource and we provide our database to the community including registered faces, hand labeled and automated disambiguations, processed captions, meta data and evaluation protocols. Our best probabilistic extraction pipeline yields an expected average accuracy of 77\% compared to image only and text only baselines which yield 63\% and 66\% respectively.
【Keywords】: visual information extraction ; data mining ; probabilistic models
【Paper Link】 【Pages】:59-65
【Authors】: Xia Hu ; Jiliang Tang ; Huan Liu
【Abstract】: The explosive use of social media also makes it a popular platform for malicious users, known as social spammers, to overwhelm normal users with unwanted content. One effective way for social spammer detection is to build a classifier based on content and social network information. However, social spammers are sophisticated and adaptable to game the system with fast evolving content and network patterns. First, social spammers continually change their spamming content patterns to avoid being detected. Second, reflexive reciprocity makes it easier for social spammers to establish social influence and pretend to be normal users by quickly accumulating a large number of "human" friends. It is challenging for existing anti-spamming systems based on batch-mode learning to quickly respond to newly emerging patterns for effective social spammer detection. In this paper, we present a general optimization framework to collectively use content and network information for social spammer detection, and provide the solution for efficient online processing. Experimental results on Twitter datasets confirm the effectiveness and efficiency of the proposed framework.
【Keywords】: social spammer; online learning; spam; social media; social networks
【Paper Link】 【Pages】:66-72
【Authors】: Zhiting Hu ; Junjie Yao ; Bin Cui
【Abstract】: Temporal online content becomes the zeitgeist to reflect our interests and changes. Active users are essential participants and promoters behind it. Temporal dynamics becomes a viable way to investigate users. However, most current work only use global temporal trend and fail to distinguish such fine-grained patterns across groups. Different users have diverse interest and exhibit distinct behaviors, and temporal dynamics tend to be different. This paper proposes GrosToT (Group Specific Topics-over-Time), a unified probabilistic model to infer latent user groups and temporal topics at the same time. It models group-specific temporal topic variation from social content. By leveraging the comprehensive group-specific temporal patterns, GrosToT significantly outperforms state-of-the-art dynamics modeling methods. Our proposed approach shows advantage not only in temporal dynamics but also group content modeling. The dynamics over different groups vary, reflecting the groups' intention. GrosToT uncovers the interplay between group interest and temporal dynamics. Specifically, groups' attention to their medium-interested topics are event-driven, showing rich bursts; while its engagement in group's dominating topics are interest-driven, remaining stable over time.
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【Paper Link】 【Pages】:73-79
【Authors】: Yu-Gang Jiang ; Baohan Xu ; Xiangyang Xue
【Abstract】: User-generated video collections are expanding rapidly in recent years, and systems for automatic analysis of these collections are in high demands. While extensive research efforts have been devoted to recognizing semantics like "birthday party" and "skiing", little attempts have been made to understand the emotions carried by the videos, e.g., "joy" and "sadness". In this paper, we propose a comprehensive computational framework for predicting emotions in user-generated videos. We first introduce a rigorously designed dataset collected from popular video-sharing websites with manual annotations, which can serve as a valuable benchmark for future research. A large set of features are extracted from this dataset, ranging from popular low-level visual descriptors, audio features, to high-level semantic attributes. Results of a comprehensive set of experiments indicate that combining multiple types of features---such as the joint use of the audio and visual clues---is important, and attribute features such as those containing sentiment-level semantics are very effective.
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【Paper Link】 【Pages】:80-86
【Authors】: Yong-Bin Kang ; Jeff Z. Pan ; Shonali Krishnaswamy ; Wudhichart Sawangphol ; Yuan-Fang Li
【Abstract】: For expressive ontology languages such as OWL 2 DL, classification is a computationally expensive task—2NEXPTIME-complete in the worst case. Hence, it is highly desirable to be able to accurately estimate classification time, especially for large and complex ontologies. Recently, machine learning techniques have been successfully applied to predicting the reasoning hardness category for a given (ontology, reasoner) pair. In this paper, we further develop predictive models to estimate actual classification time using regression techniques, with ontology metrics as features. Our large-scale experiments on 6 state-of-the-art OWL 2 DL reasoners and more than 450 significantly diverse ontologies demonstrate that the prediction models achieve high accuracy, good generalizability and statistical significance. Such prediction models have a wide range of applications. We demonstrate how they can be used to efficiently and accurately identify performance hotspots in a large and complex ontology, an otherwise very time-consuming and resource-intensive task.
【Keywords】: ontology; reasoning; prediction; performance hotspots
【Paper Link】 【Pages】:87-93
【Authors】: Freddy Lécué
【Abstract】: Diagnosis, or the process of identifying the nature and cause of an anomaly in an ontology, has been largely studied by the Semantic Web community. In the context of ontology stream, diagnosis results are not captured by a unique fixed ontology but numerous time-evolving ontologies. Thus any anomaly can be diagnosed by a large number of different explana- tions depending on the version and evolution of the ontology. We address the problems of identifying, representing, exploiting and exploring the evolution of diagnoses representations. Our approach consists in a graph-based representation, which aims at (i) efficiently organizing and linking time-evolving di- agnoses and (ii) being used for scalable exploration. The ex- periments have shown scalable diagnoses exploration in the context of real and live data from Dublin City.
【Keywords】: knowledge evolution; ontology stream; reasoning; diagnosis; scalable exploration
【Paper Link】 【Pages】:94-100
【Authors】: Sanghoon Lee ; Seung-won Hwang
【Abstract】: We study the problem of instance alignment between knowledge bases (KBs). Existing approaches, exploiting the “symmetry” of structure and information across KBs, suffer in the presence of asymmetry, which is frequent as KBs are independently built. Specifically, we observe three types of asymmetries (in concepts, in features, and in structures). Our goal is to identify key techniques to reduce accuracy loss caused by each type of asymmetry, then design Asymmetry-Resistant Instance Alignment framework (ARIA). ARIA uses two-phased blocking methods considering concept and feature asymmetries, with a novel similarity measure overcoming structure asymmetry. Compared to a state-of-the-art method, ARIA increased precision by 19% and recall by 2%, and decreased processing time by more than 80% in matching large-scale real-life KBs.
【Keywords】: alignment; matching; resolution; knowledge base
【Paper Link】 【Pages】:101-107
【Authors】: Liangda Li ; Hongyuan Zha
【Abstract】: Efficient and effective learning of social infectivity presents a critical challenge in modeling diffusion phenomena in social networks and other applications.Existing methods require substantial amount of event cascades to guarantee the learning accuracy and they only consider time-invariant infectivity.Our paper overcomes those two drawbacks by constructing a more compact model and parameterizing the infectivity using time-varying features, thus dramatically reduces the data requirement, and enables the learning of time-varying infectivity which also takes into account the underlying network topology.We replace the pairwise infectivity in the multidimensional Hawkes processes with linear combinations of those time-varying features, and optimize the associated coefficients with lasso-type of regularization. To efficiently solve the resulting optimization problem, we employ the technique of alternating direction method of multipliers which allows independent updating of the individual coefficients by optimizing a surrogate function upper-bounding the original objective function. On both synthetic and real world data, the proposed method performs better than alternatives in terms of both recovering the hidden diffusion network and predicting the occurrence time of social events.
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【Paper Link】 【Pages】:108-114
【Authors】: Xin Li ; Yiqun Liu ; Min Zhang ; Shaoping Ma
【Abstract】: "Fraudulent support phones" refers to the misleading telephone numbers placed on Web pages or other media that claim to provide services with which they are not associated. Most fraudulent support phone information is found on search engine result pages (SERPs), and such information substantially degrades the search engine user experience. In this paper, we propose an approach to identify fraudulent support telephone numbers on the Web based on the co-occurrence relations between telephone numbers that appear on SERPs. We start from a small set of seed official support phone numbers and seed fraudulent numbers. Then, we construct a co-occurrence graph according to the co-occurrence relationships of the telephone numbers that appear on Web pages. Additionally, we take the page layout information into consideration on the assumption that telephone numbers that appear in nearby page blocks should be regarded as more closely related. Finally, we develop a propagation algorithm to diffuse the trust scores of seed official support phone numbers and the distrust scores of the seed fraudulent numbers on the co-occurrence graph to detect additional fraudulent numbers. Experimental results based on over 1.5 million SERPs produced by a popular Chinese commercial search engine indicate that our approach outperforms TrustRank, Anti-TrustRank and Good-Bad Rank algorithms by achieving an AUC value of over 0.90.
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【Paper Link】 【Pages】:115-121
【Authors】: Xiaohua Liu ; Arbi Bouchoucha ; Alessandro Sordoni ; Jian-Yun Nie
【Abstract】: Diversified query expansion (DQE) based approaches aim to select a set of expansion terms with less redundancy among them while covering as many query aspects as possible. Recently they have experimentally demonstrate their effectiveness for the task of search result diversification. One challenge faced by existing DQE approaches is how to ensure the aspect coverage. In this paper, we propose a novel method for DQE, called compact aspect embedding, which exploits trace norm regularization to learn a low rank vector space for the query, with each eigenvector of the learnt vector space representing an aspect, and the absolute value of its corresponding eigenvalue representing the association strength of that aspect to the query. Meanwhile, each expansion term is mapped into the vector space as well. Based on this novel representation of the query aspects and expansion terms, we design a greedy selection strategy to choose a set of expansion terms to explicitly cover all possible aspects of the query.We test our method on several TREC diversification data sets, and show that our method significantly outperforms the state-of-the-art search result diversification approaches.
【Keywords】: query expansion; aspect embedding
【Paper Link】 【Pages】:122-128
【Authors】: Zhongqi Lu ; Yin Zhu ; Sinno Jialin Pan ; Evan Wei Xiang ; Yujing Wang ; Qiang Yang
【Abstract】: Transfer learning uses relevant auxiliary data to help the learning task in a target domain where labeled data is usually insufficient to train an accurate model. Given appropriate auxiliary data, researchers have proposed many transfer learning models. How to find such auxiliary data, however, is of little research so far. In this paper, we focus on the problem of auxiliary data retrieval, and propose a transfer learning framework that effectively selects helpful auxiliary data from an open knowledge space (e.g. the World Wide Web). Because there is no need of manually selecting auxiliary data for different target domain tasks, we call our framework Source Free Transfer Learning (SFTL). For each target domain task, SFTL framework iteratively queries for the helpful auxiliary data based on the learned model and then updates the model using the retrieved auxiliary data. We highlight the automatic constructions of queries and the robustness of the SFTL framework. Our experiments on 20NewsGroup dataset and a Google search snippets dataset suggest that the framework is capable of achieving comparable performance to those state-of-the-art methods with dedicated selections of auxiliary data.
【Keywords】: Transfer Learning;Auxiliary Data Retrieval;Text Classification
【Paper Link】 【Pages】:129-137
【Authors】: Boris Motik ; Yavor Nenov ; Robert Piro ; Ian Horrocks ; Dan Olteanu
【Abstract】: We present a novel approach to parallel materialisation (i.e., fixpoint computation) of datalog programs in centralised, main-memory, multi-core RDF systems. Our approach comprises an algorithm that evenly distributes the workload to cores, and an RDF indexing data structure that supports efficient, 'mostly' lock-free parallel updates. Our empirical evaluation shows that our approach parallelises computation very well: with 16 physical cores, materialisation can be up to 13.9 times faster than with just one core.
【Keywords】: datalog; materialization; multi-core; parallel algorithms
【Paper Link】 【Pages】:138-144
【Authors】: Naoto Ohsaka ; Takuya Akiba ; Yuichi Yoshida ; Ken-ichi Kawarabayashi
【Abstract】: Influence maximization is a problem to find small sets of highly influential individuals in a social network to maximize the spread of influence under stochastic cascade models of propagation. Although the problem has been well-studied, it is still highly challenging to find solutions of high quality in large-scale networks of the day. While Monte-Carlo-simulation-based methods produce near-optimal solutions with a theoretical guarantee, they are prohibitively slow for large graphs. As a result, many heuristic methods without any theoretical guarantee have been developed, but all of them substantially compromise solution quality. To address this issue, we propose a new method for the influence maximization problem. Unlike other recent heuristic methods, the proposed method is a Monte-Carlo-simulation-based method, and thus it consistently produces solutions of high quality with the theoretical guarantee. On the other hand, unlike other previous Monte-Carlo-simulation-based methods, it runs as fast as other state-of-the-art methods, and can be applied to large networks of the day. Through our extensive experiments, we demonstrate the scalability and the solution quality of the proposed method.
【Keywords】: influence maximization; independent cascade model; social networks
【Paper Link】 【Pages】:145-151
【Authors】: Zhi Qiao ; Peng Zhang ; Yanan Cao ; Chuan Zhou ; Li Guo ; Binxing Fang
【Abstract】: With the rapid growth of event-based social networks (EBSNs) like Meetup, the demand for event recommendation becomes increasingly urgent. In EBSNs, event recommendation plays a central role in recommending the most relevant events to users who are likely to participate in. Different from traditional recommendation problems, event recommendation encounters three new types of information, i.e., heterogenous online+offline social relationships, geographical features of events and implicit rating data from users. Yet combining the three types of data for offline event recommendation has not been considered. Therefore, we present a Bayesian latent factor model that can unify these data for event recommendation. Experimental results on real-world data sets show the performance of our method.
【Keywords】: event recommendation
【Paper Link】 【Pages】:152-158
【Authors】: Adish Singla ; Eric Horvitz ; Ece Kamar ; Ryen White
【Abstract】: Online services such as web search and e-commerce applications typically rely on the collection of data about users, including details of their activities on the web. Such personal data is used to maximize revenues via targeting of advertisements and longer engagements of users, and to enhance the quality of service via personalization of content. To date, service providers have largely followed the approach of either requiring or requesting consent for collecting user data. Users may be willing to share private information in return for incentives, enhanced services, or assurances about the nature and extent of the logged data. We introduce stochastic privacy, an approach to privacy centering on the simple concept of providing people with a guarantee that the probability that their personal data will be shared does not exceed a given bound. Such a probability, which we refer to as the privacy risk, can be given by users as a preference or communicated as a policy by a service provider. Service providers can work to personalize and to optimize revenues in accordance with preferences about privacy risk. We present procedures, proofs, and an overall system for maximizing the quality of services, while respecting bounds on privacy risk. We demonstrate the methodology with a case study and evaluation of the procedures applied to web search personalization. We show how we can achieve near-optimal utility of accessing information with provable guarantees on the probability of sharing data.
【Keywords】: privacy tradeoff; value of information; online services; web search personalization; submodular optimization
【Paper Link】 【Pages】:159-165
【Authors】: Shulong Tan ; Ziyu Guan ; Deng Cai ; Xuzhen Qin ; Jiajun Bu ; Chun Chen
【Abstract】: Nowadays many people are members of multiple online social networks simultaneously, such as Facebook, Twitter and some other instant messaging circles. But these networks are usually isolated from each other. Mapping common users across these social networks will benefit many applications. Methods based on username comparison perform well on parts of users, however they can not work in the following situations: (a) users choose different usernames in different networks; (b) a unique username corresponds to different individuals. In this paper, we propose to utilize social structures to improve the mapping performance. Specifically, a novel subspace learning algorithm, Manifold Alignment on Hypergraph (MAH), is proposed. Different from traditional semi-supervised manifold alignment methods, we use hypergraph to model high-order relations here. For a target user in one network, the proposed algorithm ranks all users in the other network by their possibilities of being the corresponding user. Moreover, methods based on username comparison can be incorporated into our algorithm easily to further boost the mapping accuracy. Experimental results have demonstrated the effectiveness of our proposed algorithm in mapping users across networks.
【Keywords】: Social Networks; Manifold Alignment; Hypergraph; De-anonymization; User Mapping
【Paper Link】 【Pages】:166-172
【Authors】: Niket Tandon ; Gerard de Melo ; Gerhard Weikum
【Abstract】: Applications are increasingly expected to make smart decisions based on what humans consider basic commonsense. An often overlooked but essential form of commonsense involves comparisons, e.g. the fact that bears are typically more dangerous than dogs, that tables are heavier than chairs, or that ice is colder than water. In this paper, we first rely on open information extraction methods to obtain large amounts of comparisons from the Web. We then develop a joint optimization model for cleaning and disambiguating this knowledge with respect to WordNet. This model relies on integer linear programming and semantic coherence scores. Experiments show that our model outperforms strong baselines and allows us to obtain a large knowledge base of disambiguated commonsense assertions.
【Keywords】: knowledge base construction; commonsense knowledge
【Paper Link】 【Pages】:173-179
【Authors】: Beidou Wang ; Martin Ester ; Jiajun Bu ; Deng Cai
【Abstract】: Social explanation, the statement with the form of "A and B also like the item", is widely used in almost all the major recommender systems in the web and effectively improves the persuasiveness of the recommendation results by convincing more users to try. This paper presents the first algorithm to generate the most persuasive social explanation by recommending the optimal set of users to be put in the explanation. New challenges like modeling persuasiveness of multiple users, different types of users in social network, sparsity of likes, are discussed in depth and solved in our algorithm. The extensive evaluation demonstrates the advantage of our proposed algorithm compared with traditional methods.
【Keywords】: Social Explanation; Persuasiveness Modeling; Recommender System;
【Paper Link】 【Pages】:180-186
【Authors】: Zhigang Wang ; Juanzi Li ; Shuangjie Li ; Mingyang Li ; Jie Tang ; Kuo Zhang ; Kun Zhang
【Abstract】: Creating knowledge bases based on the crowd-sourced wikis, like Wikipedia, has attracted significant research interest in the field of intelligent Web. However, the derived taxonomies usually contain many mistakenly imported taxonomic relations due to the difference between the user-generated subsumption relations and the semantic taxonomic relations. Current approaches to solving the problem still suffer the following issues: (i) the heuristic-based methods strongly rely on specific language dependent rules. (ii) the corpus-based methods depend on a large-scale high-quality corpus, which is often unavailable. In this paper, we formulate the cross-lingual taxonomy derivation problem as the problem of cross-lingual taxonomic relation prediction. We investigate different linguistic heuristics and language independent features, and propose a cross-lingual knowledge validation based dynamic adaptive boosting model to iteratively reinforce the performance of taxonomic relation prediction. The proposed approach successfully overcome the above issues, and experiments show that our approach significantly outperforms the designed state-of-the-art comparison methods.
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【Paper Link】 【Pages】:187-193
【Authors】: Shiyang Wen ; Xiaojun Wan
【Abstract】: This paper studies the problem of emotion classification in microblog texts. Given a microblog text which consists of several sentences, we classify its emotion as anger, disgust, fear, happiness, like, sadness or surprise if available. Existing methods can be categorized as lexicon based methods or machine learning based methods. However, due to some intrinsic characteristics of the microblog texts, previous studies using these methods always get unsatisfactory results. This paper introduces a novel approach based on class sequential rules for emotion classification of microblog texts. The approach first obtains two potential emotion labels for each sentence in a microblog text by using an emotion lexicon and a machine learning approach respectively, and regards each microblog text as a data sequence. It then mines class sequential rules from the dataset and finally derives new features from the mined rules for emotion classification of microblog texts. Experimental results on a Chinese benchmark dataset show the superior performance of the proposed approach.
【Keywords】: Emotion Classification; Class Sequential Rule
【Paper Link】 【Pages】:194-200
【Authors】: Ou Wu ; Ruiguang Hu ; Xue Mao ; Weiming Hu
【Abstract】: The types of web data vary in terms of information quantity and quality. For example, some pages contain numerous texts, whereas some others contain few texts; some web videos are in high resolution, whereas some other web videos are in low resolution. As a consequence, the quality of extracted features from different web data may also vary greatly. Existing learning algorithms on web data classification usually ignore the variations of information quality or quantity. In this paper, the information quantity and quality of web data are described by quality-related factors such as text length and image quantity, and a new learning method is proposed to train classifiers based on quality-related factors. The method divides training data into subsets according to the clustering results of quality-related factors and then trains classifiers by using a multi-task learning strategy for each subset. Experimental results indicate that the quality-related factors are useful in web data classification, and the proposed method outperforms conventional algorithms that do not consider information quantity and quality.
【Keywords】: Quality; Classification; Multi-task learning
【Paper Link】 【Pages】:201-207
【Authors】: Wenxuan Xie ; Yuxin Peng ; Jianguo Xiao
【Abstract】: Nowadays images on social networking websites (e.g., Flickr) are mostly accompanied with user-contributed tags, which help cast a new light on the conventional content-based image analysis tasks such as image classification and retrieval. In order to establish a scalable social image analysis system, two issues need to be considered: 1) Supervised learning is a futile task in modeling the enormous number of concepts in the world, whereas unsupervised approaches overcome this hurdle; 2) Algorithms are required to be both spatially and temporally efficient to handle large-scale datasets. In this paper, we propose a cross-view feature learning (CVFL) framework to handle the problem of social image analysis effectively and efficiently. Through explicitly modeling the relevance between image content and tags (which is empirically shown to be visually and semantically meaningful), CVFL yields more promising results than existing methods in the experiments. More importantly, being general and descriptive, CVFL and its variants can be readily applied to other large-scale multi-view tasks in unsupervised setting.
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【Paper Link】 【Pages】:208-214
【Authors】: Jae-Bong Yoo ; Jihie Kim
【Abstract】: We introduce a new application of online dialogue analysis: supporting pedagogical assessment of online Q&A discussions. Extending the existing speech act framework, we capture common emotional expressions that often appear in student discussions, such as frustration and degree of certainty, and present a viable approach for the classification. We demonstrate how such dialogue information can be used in analyzing student discussions and identifying difficulties. In particular, the difficulty expressions are aligned to discussion patterns and student performance. We found that frustration occurs more frequently in longer discussions. The students who frequently express frustration tend to get lower grades than others. On the other hand, frequency of high certainty expressions is positively correlated with the performance. We expect such online dialogue analyses can become a powerful assessment tool for instructors and education researchers.
【Keywords】: Difficulty Expressions; Emotional/Information Roles; Discussion Development; Performance Prediction
【Paper Link】 【Pages】:215-221
【Authors】: Hongliang Yu ; Zhi-Hong Deng ; Yunlun Yang ; Tao Xiong
【Abstract】: As an effective technology for navigating a large number of images, image summarization is becoming a promising task with the rapid development of image sharing sites and social networks. Most existing summarization approaches use the visual-based features for image representation without considering tag information.In this paper, we propose a novel framework, named JOINT, which employs both image content and tag information to summarize images. Our model generates the summary images which can best reconstruct the original collection. Based on the assumption that an image with representative content should also have typical tags, we introduce a similarity-inducing regularizer to our model. Furthermore, we impose the lasso penalty on the objective function to yield a concise summary set. Extensive experiments demonstrate our model outperforms the state-of-the-art approaches.
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【Paper Link】 【Pages】:222-228
【Authors】: Ting Yuan ; Jian Cheng ; Xi Zhang ; Shuang Qiu ; Hanqing Lu
【Abstract】: Recently, some recommendation methods try to improvethe prediction results by integrating informationfrom user’s multiple types of behaviors. How to modelthe dependence and independence between differentbehaviors is critical for them. In this paper, we proposea novel recommendation model, the Group-Sparse MatrixFactorization (GSMF), which factorizes the ratingmatrices for multiple behaviors into the user and itemlatent factor space with group sparsity regularization.It can (1) select out the different subsets of latent factorsfor different behaviors, addressing that users’ decisionson different behaviors are determined by differentsets of factors;(2) model the dependence and independencebetween behaviors by learning the sharedand private factors for multiple behaviors automatically; (3) allow the shared factors between different behaviorsto be different, instead of all the behaviors sharingthe same set of factors. Experiments on the real-world dataset demonstrate that our model can integrate users’multiple types of behaviors into recommendation better,compared with other state-of-the-arts.
【Keywords】: recommender systems; Matrix Factorization
【Paper Link】 【Pages】:229-236
【Authors】: Jun Zhang ; Chaokun Wang ; Jianmin Wang
【Abstract】: Social influence has been widely accepted to explain people's cascade behaviors and further utilized in many related applications. However, few of existing work studied the direct, microscopic and temporal impact of social influence on people's behaviors in detail. In this paper we concentrate on the behavior modeling and systematically formulate the family of behavior propagation models (BPMs) including the static models (BP and IBP), and their discrete temporal variants (DBP and DIBP). To address the temporal dynamics of behavior propagation over continuous time, we propose a continuous temporal interest-aware behavior propagation model, called CIBP. As a new member of the BPM family, CIBP exploits the continuous-temporal functions (CTFs) to model the fully-continuous dynamic variance of social influence over time. Experiments on real-world datasets evaluated the family of BPMs and demonstrated the effectiveness of our proposed approach.
【Keywords】: CIBP; Behavior Propagation; Behavior Propagation Models; Temporal Dynamics; Social Influence; Behavior Prediction; Social Network
【Paper Link】 【Pages】:237-244
【Authors】: Xiaoming Zheng ; Yan Wang ; Mehmet A. Orgun ; Youliang Zhong ; Guanfeng Liu
【Abstract】: Online social networks have been used for a variety of rich activities in recent years, such as investigating potential employees and seeking recommendations of high quality services and service providers. In such activities, trust is one of the most critical factors for the decision-making of users. In the literature, the state-of-the-art trust prediction approaches focus on either dispositional trust tendency and propagated trust of the pair-wise trust relationships along a path or the similarity of trust rating values. However, there are other influential factors that should be taken into account, such as the similarity of the trust rating distributions. In addition, tendency, propagated trust and similarity are of different types, as either personal properties or interpersonal properties. But the difference has been neglected in existing models. Therefore, in trust prediction, it is necessary to take all the above factors into consideration in modeling, and process them separately and differently. In this paper we propose a new trust prediction model based on trust decomposition and matrix factorization, considering all the above influential factors and differentiating both personal and interpersonal properties. In this model, we first decompose trust into trust tendency and tendency-reduced trust. Then, based on tendency-reduced trust ratings, matrix factorization with a regularization term is leveraged to predict the tendency-reduced values of missing trust ratings, incorporating both propagated trust and the similarity of users' rating habits. In the end, the missing trust ratings are composed with predicted tendency-reduced values and trust tendency values. Experiments conducted on a real-world dataset illustrate significant improvement delivered by our approach in trust prediction accuracy over the state-of-the-art approaches.
【Keywords】: Trust Prediction; Social Network; Trust Propagation; Trust Tendency
【Paper Link】 【Pages】:245-252
【Authors】: Chris Alvin ; Sumit Gulwani ; Rupak Majumdar ; Supratik Mukhopadhyay
【Abstract】: This paper presents a semi-automated methodology for generating geometric proof problems of the kind found in a high-school curriculum. We formalize the notion of a geometry proof problem and describe an algorithm for generating such problems over a user-provided figure. Our experimental results indicate that our problem generation algorithm can effectively generate proof problems in elementary geometry. On a corpus of 110 figures taken from popular geometry textbooks, our system generated an average of about 443 problems per figure in an average time of 4.7 seconds per figure.
【Keywords】: Problem Synthesis, Automated Reasoning, Computer-Aided Education, Interesting Problem, Complete Problem
【Paper Link】 【Pages】:253-261
【Authors】: Michael Anderson ; Susan Leigh Anderson
【Abstract】: We contend that ethically significant behavior of autonomous systems should be guided by explicit ethical principles determined through a consensus of ethicists. To provide assistance in developing these ethical principles, we have developed GenEth, a general ethical dilemma analyzer that, through a dialog with ethicists, codifies ethical principles in any given domain. GenEth has been used to codify principles in a number of domains pertinent to the behavior of autonomous systems and these principles have been verified using an Ethical Turing Test.
【Keywords】: machine ethics; application; machine learning; inductive logic programming
【Paper Link】 【Pages】:262-268
【Authors】: Bartosz Boguslawski ; Vincent Gripon ; Fabrice Seguin ; Frédéric Heitzmann
【Abstract】: Associative memories are data structures that allow retrieval of previously stored messages given part of their content. They thus behave similarly to human brain's memory that is capable for instance of retrieving the end of a song given its beginning. Among different families of associative memories, sparse ones are known to provide the best efficiency (ratio of the number of bits stored to that of bits used). Nevertheless, it is well known that non-uniformity of the stored messages can lead to dramatic decrease in performance. Recently, a new family of sparse associative memories achieving almost-optimal efficiency has been proposed. Their structure induces a direct mapping between input messages and stored patterns. In this work, we show the impact of non-uniformity on the performance of this recent model and we exploit the structure of the model to introduce several strategies to allow for efficient storage of non-uniform messages. We show that a technique based on Huffman coding is the most efficient.
【Keywords】: neural clique; sparsity; associative memory; non-uniform distribution; compression code
【Paper Link】 【Pages】:269-275
【Authors】: Yoon-Sik Cho ; Greg Ver Steeg ; Aram Galstyan
【Abstract】: The emergence of location based social network (LBSN) services makes it possible to study individuals’ mobility patterns at a fine-grained level and to see how they are impacted by social factors. In this study we analyze the check-in patterns in LBSN and observe significant temporal clustering of check-in activities. We explore how self-reinforcing behaviors, social factors, and exogenous effects contribute to this clustering and introduce a framework to distinguish these effects at the level of individual check-ins for both users and venues. Using check-in data from three major cities, we show not only that our model can improve prediction of future check-ins, but also that disentangling of different factors allows us to infer meaningful properties of different venues.
【Keywords】: Location Based Social Network; Point Processes; Temporal Clustering; Social Network Analysis
【Paper Link】 【Pages】:276-282
【Authors】: William Herlands ; Ricky Der ; Yoel Greenberg ; Simon A. Levin
【Abstract】: Recent literature has demonstrated the difficulty of classifying between composers who write in extremely similar styles (homogeneous style). Additionally, machine learning studies in this field have been exclusively of technical import with little musicological interpretability or significance. We present a supervised machine learning system which addresses the difficulty of differentiating between stylistically homogeneous composers using foundational elements of music, their complexity and interaction. Our work expands on previous style classification studies by developing more complex features as well as introducing a new class of musical features which focus on local irregularities within musical scores. We demonstrate the discriminative power of the system as applied to Haydn and Mozart's string quartets. Our results yield interpretable musicological conclusions about Haydn's and Mozart's stylistic differences while distinguishing between the composers with higher accuracy than previous studies in this domain.
【Keywords】: Supervised machine learning; machine learning; music; Classical music; music; Mozart; Haydn; information retrieval
【Paper Link】 【Pages】:283-290
【Authors】: Mohammad Raza ; Sumit Gulwani ; Natasa Milic-Frayling
【Abstract】: Recent advances in Programming by Example (PBE) have supported new applications to text editing, but existing approaches are limited to simple text strings. In this paper we address transformations in richly formatted documents, using an approach based on the idea of least general generalizations from inductive inference, which avoids the scalability issues faced by state-of-the-art PBE methods. We describe a novel domain specific language (DSL) that expresses transformations over XML structures describing richly formatted content, and a synthesis algorithm that generates a minimal program with respect to a natural subsumption ordering in our DSL. We present experimental results on tasks collected from online help forums, showing an average of 4.17 examples required for task completion.
【Keywords】: Programming by example, program synthesis, inductive inference, xml formats
【Paper Link】 【Pages】:291-297
【Authors】: Hua-Wei Shen ; Dashun Wang ; Chaoming Song ; Albert-László Barabási
【Abstract】: An ability to predict the popularity dynamics of individual items within a complex evolving system has important implications in an array of areas. Here we propose a generative probabilistic framework using a reinforced Poisson process to explicitly model the process through which individual items gain their popularity. This model distinguishes itself from existing models via its capability of modeling the arrival process of popularity and its remarkable power at predicting the popularity of individual items. It possesses the flexibility of applying Bayesian treatment to further improve the predictive power using a conjugate prior. Extensive experiments on a longitudinal citation dataset demonstrate that this model consistently outperforms existing popularity prediction methods.
【Keywords】: Social Dynamics; Popularity Prediction; Reinforced Poisson Process
【Paper Link】 【Pages】:298-305
【Authors】: Michael David Taylor
【Abstract】: Estimating the remaining energy in high-capacity electric vehicle batteries is essential to safe and efficient operation. Accurate estimation remains a major challenge, however, because battery state cannot be observed directly. In this paper, we demonstrate a method for estimating battery remaining energy using real data collected from the Charge Car electric vehicle. This new method relies on energy integration as an initial estimation step, which is then corrected using a neural net that learns how error accumulates from recent charge/discharge cycles. In this way, the algorithm is able to adapt to nonlinearities and variations that are difficult to model or characterize. On the collected dataset, this method is demonstrated to be accurate to within 2.5% to 5% of battery remaining energy, which equates to approximately 1 to 2 miles of residual range for the Charge Car given its 10kWh battery pack.
【Keywords】: electric vehicles; neural networks; battery state estimation; coulomb counting; joule counting
【Paper Link】 【Pages】:306-312
【Authors】: Yang Yang ; Jia Jia ; Shumei Zhang ; Boya Wu ; Qicong Chen ; Juanzi Li ; Chunxiao Xing ; Jie Tang
【Abstract】: Extracting emotions from images has attracted much interest, in particular with the rapid development of social networks. The emotional impact is very important for understanding the intrinsic meanings of images. Despite many studies having been done, most existing methods focus on image content, but ignore the emotion of the user who published the image. One interesting question is: How does social effect correlate with the emotion expressed in an image? Specifically, can we leverage friends interactions (e.g., discussions) related to an image to help extract the emotions? In this paper, we formally formalize the problem and propose a novel emotion learning method by jointly modeling images posted by social users and comments added by their friends. One advantage of the model is that it can distinguish those comments that are closely related to the emotion expression for an image from the other irrelevant ones. Experiments on an open Flickr dataset show that the proposed model can significantly improve (+37.4% by F1) the accuracy for inferring user emotions. More interestingly, we found that half of the improvements are due to interactions between 1.0% of the closest friends.
【Keywords】: emotion inference; images and comments; generative model
【Paper Link】 【Pages】:313-319
【Authors】: Yang Yang ; Walter Luyten ; Lu Liu ; Marie-Francine Moens ; Jie Tang ; Juanzi Li
【Abstract】: Diabetes complications often afflict diabetes patients seriously: over 68% of diabetes-related mortality is caused by diabetes complications. In this paper, we study the problem of automatically diagnosing diabetes complications from patients' lab test results. The objective problem has two main challenges: 1) feature sparseness: a patient only undergoes 1.26% lab tests on average, and 65.5% types of lab tests are performed on samples from less than 10 patients; 2) knowledge skewness: it lacks comprehensive detailed domain knowledge of the association between diabetes complications and lab tests. To address these challenges, we propose a novel probabilistic model called Sparse Factor Graph Model (SparseFGM). SparseFGM projects sparse features onto a lower-dimensional latent space, which alleviates the problem of sparseness. SparseFGM is also able to capture the associations between complications and lab tests, which help handle the knowledge skewness. We evaluate the proposed model on a large collections of real medical records. SparseFGM outperforms (+20% by F1) baselines significantly and gives detailed associations between diabetes complications and lab tests.
【Keywords】: forecast diabetes complications; feature sparseness; sparse factor graph
【Paper Link】 【Pages】:320-328
【Authors】: Feng Zhang ; Victor E. Lee ; Ruoming Jin
【Abstract】: For datasets in Collaborative Filtering (CF) recommendations, even if the identifier is deleted and some trivial perturbation operations are applied to ratings before they are released, there are research results claiming that the adversary could discriminate the individual's identity with a little bit of information. In this paper, we propose $k$-coRating, a novel privacy-preserving model, to retain data privacy by replacing some null ratings with "well-predicted" scores. They do not only mask the original ratings such that a $k$-anonymity-like data privacy is preserved, but also enhance the data utility (measured by prediction accuracy in this paper), which shows that the traditional assumption that accuracy and privacy are two goals in conflict is not necessarily correct. We show that the optimal $k$-coRated mapping is an NP-hard problem and design a naive but efficient algorithm to achieve $k$-coRating. All claims are verified by experimental results.
【Keywords】: Privacy-preserving Collaborative Filtering Recommender Systems; Data Privacy; Parallel Computing
【Paper Link】 【Pages】:329-335
【Authors】: Hyungil Ahn ; Rosalind W. Picard
【Abstract】: In this paper we computationally examine how subjective experience may help or harm the decision maker's learning under uncertain outcomes, frames and their interactions. To model subjective experience, we propose the "experienced-utility function" based on a prospect theory (PT)-based parameterized subjective value function. Our analysis and simulations of two-armed bandit tasks present that the task domain (underlying outcome distributions) and framing (reference point selection) influence experienced utilities and in turn, the "subjective discriminability" of choices under uncertainty. Experiments demonstrate that subjective discriminability improves on objective discriminability by the use of the experienced-utility function with appropriate framing for a given task domain, and that bigger subjective discriminability leads to more optimal decisions in learning under uncertainty.
【Keywords】: subjective experience-based learning; subjective value function; prospect theory; subjective discriminability; experienced utility; decision utility; gain frame; loss frame
【Paper Link】 【Pages】:336-342
【Authors】: Celso M. de Melo ; Jonathan Gratch ; Peter J. Carnevale
【Abstract】: Agency - the capacity to plan and act - and experience - the capacity to sense and feel - are two critical aspects that determine whether people will perceive non-human entities, such as autonomous agents, to have a mind. There is evidence that the absence of either can reduce cooperation. We present an experiment that tests the necessity of both for cooperation with agents. In this experiment we manipulated people's perceptions about the cognitive and affective abilities of agents, when engaging in the ultimatum game. The results indicated that people offered more money to agents that were perceived to make decisions according to their intentions (high agency), rather than randomly (low agency). Additionally, the results showed that people offered more money to agents that expressed emotion (high experience), when compared to agents that did not (low experience). We discuss the implications of this agency-experience theoretical framework for the design of artificially intelligent decision makers.
【Keywords】: Mind Perception; Decision Making; Agency; Experience
【Paper Link】 【Pages】:343-349
【Authors】: Leif Johnson ; Dana H. Ballard
【Abstract】: Efficient codes have been used effectively in both computer science and neuroscience to better understand the information processing in visual and auditory encoding and discrimination tasks. In this paper, we explore the use of efficient codes for representing information relevant to human movements during locomotion. Specifically, we apply motion capture data to a physical model of the human skeleton to compute joint angles (inverse kinematics) and joint torques (inverse dynamics); then, by treating the resulting paired dataset as a supervised regression problem, we investigate the effect of sparsity in mapping from angles to torques. The results of our investigation suggest that sparse codes can indeed represent salient features of both the kinematic and dynamic views of human locomotion movements. However, sparsity appears to be only one parameter in building a model of inverse dynamics; we also show that the "encoding" process benefits significantly by integrating with the "regression" process for this task. In addition, we show that, for this task, simple coding and decoding methods are not sufficient to model the extremely complex inverse dynamics mapping. Finally, we use our results to argue that representations of movement are critical to modeling and understanding these movements.
【Keywords】:
【Paper Link】 【Pages】:350-358
【Authors】: Josefina Sierra-Santibáñez
【Abstract】: This paper presents an agent-based model that studies the emergence and evolution of a language system of logical constructions, i.e. a vocabulary and a set of grammatical constructions that allow the expression of logical combinations of categories. The model assumes the agents have a common vocabulary for basic categories, the ability to construct logical combinations of categories using Boolean functions, and some general purpose cognitive capacities for invention, adoption, induction and adaptation. But it does not assume the agents have a vocabulary for Boolean functions nor grammatical constructions for expressing such logical combinations of categories through language. The results of the experiments we have performed show that a language system of logical constructions emerges as a result of a process of self-organisation of the individual agents' interactions when these agents adapt their preferences for vocabulary and grammatical constructions to those they observe are used more often by the rest of the population, and that such a language system is transmitted from one generation to the next.
【Keywords】: Cognitive Modeling; Symbolic AI; Simulating Humans; Adaptive Behavior
【Paper Link】 【Pages】:359-365
【Authors】: Vinay K. Chaudhri ; Stijn Heymans ; Adam Overholtzer ; Aaron Spaulding ; Michael A. Wessel
【Abstract】: Cognitive simulation of analogical processing can be used to answer comparison questions such as: What are the similarities and/or differences between A and B, for concepts A and B in a knowledge base (KB). Previous attempts to use a general-purpose analogical reasoner to answer such questions revealed three major problems: (a) the system presented too much information in the answer, and the salient similarity or difference was not highlighted; (b) analogical inference found some incorrect differences; and (c) some expected similarities were not found. The cause of these problems was primarily a lack of a well-curated KB and, and secondarily, algorithmic deficiencies. In this paper, relying on a well-curated biology KB, we present a specific implementation of comparison questions inspired by a general model of analogical reasoning. We present numerous examples of answers produced by the system and empirical data on answer quality to illustrate that we have addressed many of the problems of the previous system.
【Keywords】: analogical reasoning, knowledge based systems, question answering, qualitative reasoning
【Paper Link】 【Pages】:366-372
【Authors】: Suren Kumar ; Vikas Dhiman ; Jason J. Corso
【Abstract】: Various perceptual domains have underlying compositional semantics that are rarely captured in current models. We suspect this is because directly learning the compositional structure has evaded these models. Yet, the compositional structure of a given domain can be grounded in a separate domain thereby simplifying its learning. To that end, we propose a new approach to modeling bimodal percepts that explicitly relates distinct projections across each modality and then jointly learns a bimodal sparse representation. The resulting model enables compositionality across these distinct projections and hence can generalize to unobserved percepts spanned by this compositional basis. For example, our model can be trained on 'red triangles' and 'blue squares'; yet, implicitly will also have learned 'red squares' and 'blue triangles'. The structure of the projections and hence the compositional basis is learned automatically for a given language model. To test our model, we have acquired a new bimodal dataset comprising images and spoken utterances of colored shapes in a tabletop setup. Our experiments demonstrate the benefits of explicitly leveraging compositionality in both quantitative and human evaluation studies.
【Keywords】: compositional model, bimodal sparse representation, vision and audio
【Paper Link】 【Pages】:373-379
【Authors】: Clifton James McFate ; Kenneth D. Forbus ; Thomas R. Hinrichs
【Abstract】: The naturalness of qualitative reasoning suggests that qualitative representations might be an important component of the semantics of natural language. Prior work showed that frame-based representations of qualitative process theory constructs could indeed be extracted from natural language texts. That technique relied on the parser recognizing specific syntactic constructions, which had limited coverage. This paper describes a new approach, using narrative function to represent the higher-order relationships between the constituents of a sentence and between sentences in a discourse. We outline how narrative function combined with query-driven abduction enables the same kinds of information to be extracted from natural language texts. Moreover, we also show how the same technique can be used to extract type-level qualitative representations from text, and used to improve performance in playing a strategy game.
【Keywords】: qualitative reasoning;natural language; knowledge representation
【Paper Link】 【Pages】:380-386
【Authors】: Keith McGreggor ; Ashok K. Goel
【Abstract】: We report a novel approach to addressing the Raven’s Progressive Matrices (RPM) tests, one based upon purely visual representations. Our technique introduces the calculation of confidence in an answer and the automatic adjustment of level of resolution if that confidence is insufficient. We first describe the nature of the visual analogies found on the RPM. We then exhibit our algorithm and work through a detailed example. Finally, we present the performance of our algorithm on the four major variants of the RPM tests, illustrating the impact of confidence. This is the first such account of any computational model against the entirety of the Raven’s.
【Keywords】: Analogical reasoning; fractal representations; intelligence tests
【Paper Link】 【Pages】:387-394
【Authors】: Shiwali Mohan ; John E. Laird
【Abstract】: Our research aims at building interactive robots and agents that can expand their knowledge by interacting with human users. In this paper, we focus on learning goal-oriented tasks from situated interactive instructions. Learning the structure of novel tasks and how to execute them is a challenging computational problem requiring the agent to acquire a variety of knowledge including goal definitions and hierarchical control information. We frame acquisition of novel tasks as an explanation-based learning (EBL) problem and propose an interactive learning variant of EBL for a robotic agent. We show that our approach can exploit information in situated instructions along with the domain knowledge to demonstrate fast generalization on several tasks. The knowledge acquired transfers across structurally similar tasks. Finally, we show that our approach seamlessly combines agent-driven exploration with instructions for mixed-initiative learning.
【Keywords】: explanation-based learning, hierarchical task learning, situated interactive instruction, Soar cognitive architecture, learning from demonstration, intelligent interactive systems, Rosie, robot learning, human-robot interaction, interactive learning
【Paper Link】 【Pages】:395-401
【Authors】: Matthew Molineaux ; David W. Aha
【Abstract】: Agents with incomplete environment models are likely to be surprised, and this represents an opportunity to learn. We investigate approaches for situated agents to detect surprises, discriminate among different forms of surprise, and hypothesize new models for the unknown events that surprised them. We instantiate these approaches in a new goal reasoning agent (named FoolMeTwice), investigate its performance in simulation studies, and report that it produces plans with significantly reduced execution cost in comparison to not learning models for surprising events.
【Keywords】: relational learning; learning of environment models; exogenous event models;
【Paper Link】 【Pages】:402-409
【Authors】: Chris Pearce ; Ben Leon Meadows ; Pat Langley ; Mike Barley
【Abstract】: In this paper, we discuss a computational approach to the cognitivetask of social planning. First, we specify a class of planningproblems that involve an agent who attempts to achieve its goalsby altering other agents' mental states. Next, we describe SFPS,a flexible problem solver that generates social plans of this sort,including ones that include deception and reasoning about otheragents' beliefs. We report the results for experiments on socialscenarios that involve different levels of sophistication and thatdemonstrate both SFPS's capabilities and the sources of its power.Finally, we discuss how our approach to social planning has beeninformed by earlier work in the area and propose directions foradditional research on the topic.
【Keywords】: social planning, social cognition, mental state ascription, problem solving
【Paper Link】 【Pages】:410-416
【Authors】: James H. Faghmous ; Hung Nguyen ; Matthew Le ; Vipin Kumar
【Abstract】: Mesoscale ocean eddies are a critical component of the Earth System as they dominate the ocean's kinetic energy and impact the global distribution of oceanic heat, salinity, momentum, and nutrients. Therefore, accurately representing these dynamic features is critical for our planet's sustainability. The majority of methods that identify eddies from satellite observations analyze the data in a frame-by-frame basis despite the fact that eddies are dynamic objects that propagate across space and time. We introduce the notion of spatio-temporal consistency to identify eddies in a continuous spatio-temporal field, to simultaneously ensure that the features detected are both spatially and temporally consistent. Our spatio-temporal consistency approach allows us to remove most of the expert criteria used in traditional methods to reduce false negatives. The removal of arbitrary heuristics enables us to render more complete eddy dynamics by identifying smaller and longer lived eddies compared to existing methods.
【Keywords】: patio-temporal data mining;sustainability;oceanography
【Paper Link】 【Pages】:417-423
【Authors】: Stefan Funke ; Andre Nusser ; Sabine Storandt
【Abstract】: Compared to conventional cars, electric vehicles still suffer from a considerably shorter cruising range. Combined with the sparsity of battery loading stations, the complete transition to E-mobility still seems a long way to go. In this paper, we consider the problem of placing as few loading stations as possible such that on any shortest path there are enough to guarantee sufficient energy supply. This means, that EV owners no longer have to plan their trips ahead incorporating loading station locations, and are no longer forced to accept long detours to reach their destinations. We show how to model this problem and introduce heuristics which provide close-to-optimal solutions even in large road networks.
【Keywords】: E-Mobility; Facility Location; Hitting Set
【Paper Link】 【Pages】:424-430
【Authors】: Arnaud Jutzeler ; Jason Jingshi Li ; Boi Faltings
【Abstract】: Air pollution has a direct impact to human health, and data-driven air quality models are useful for evaluating population exposure to air pollutants. In this paper, we propose a novel region-based Gaussian process model for estimating urban air pollution dispersion, and applied it to a large dataset of ultrafine particle (UFP) measurements collected from a network of sensors located on several trams in the city of Zurich. We show that compared to existing grid-based models, the region-based model produces better predictions across aggregates of all time scales. The new model is appropriate for many useful user applications such as exposure assessment and anomaly detection.
【Keywords】: Air pollution; Gaussian process; ultrafine particles; spatial reasoning; region-based model
【Paper Link】 【Pages】:431-437
【Authors】: Liang Lan ; Vuk Malbasa ; Slobodan Vucetic
【Abstract】: In disease mapping, the spatial scan statistic is used to detect spatial regions where population is exposed to a significantly higher disease risk than expected. In this important application, the current residence is typically used to define the location of individuals from the population. Considering the mobility of humans at various temporal and spatial scales, using only information about the current residence may be an insufficiently informative proxy because it ignores a multitude of exposures that may occur away from home, or which had occurred at previous residences. In this paper, we propose a spatial scan statistic that is appropriate for disease mapping on mobile populations. We formulate a computationally efficient algorithm that uses the proposed statistic to find significant high-risk regions from mobile population's disease status data. The algorithm is applicable on large populations and over dense spatial grids. The experimental results demonstrate that the proposed algorithm is computationally efficient and outperforms the traditional disease clustering approaches at discovering high-risk regions in mobile populations.
【Keywords】: Disease Clustering; Spatial Scan; Mobility Data;
【Paper Link】 【Pages】:438-443
【Authors】: Ronan Le Bras ; Richard Bernstein ; John M. Gregoire ; Santosh K. Suram ; Carla P. Gomes ; Bart Selman ; R. Bruce van Dover
【Abstract】: Newly-discovered materials have been central to recent technological advances. They have contributed significantly to breakthroughs in electronics, renewable energy and green buildings, and overall, have promoted the advancement of global human welfare. Yet, only a fraction of all possible materials have been explored. Accelerating the pace of discovery of materials would foster technological innovations, and would potentially address pressing issues in sustainability, such as energy production or consumption. The bottleneck of this discovery cycle lies, however, in the analysis of the materials data. As materials scientists have recently devised techniques to efficiently create thousands of materials and experimentalists have developed new methods and tools to characterize these materials, the limiting factor has become the data analysis itself. Hence, the goal of this paper is to stimulate the development of new computational techniques for the analysis of materials data, by bringing together the complimentary expertise of materials scientists and computer scientists. In collaboration with two major research laboratories in materials science, we provide the first publicly available dataset for the phase map identification problem. In addition, we provide a parameterized synthetic data generator to assess the quality of proposed approaches, as well as tools for data visualization and solution evaluation.
【Keywords】: Materials Discovery; Computational Sustainability; Dataset;
【Paper Link】 【Pages】:444-450
【Authors】: Abraham Othman
【Abstract】: Scoring involves the compression of a number of quantitative attributes into a single meaningful value. We consider the problem of how to generate scores in a setting where they should be weakly monotone (either non-increasing or non-decreasing) in their dimensions. Our approach allows an expert to score an arbitrary set of points to produce meaningful, continuous, monotone scores over the entire domain, while exactly interpolating through those inputs. In contrast, existing monotone interpolating methods only work in two dimensions and typically require exhaustive grid input. Our technique significantly lowers the bar to score creation, allowing domain experts to develop mathematically coherent scores. The method is used in practice to create the LEED Performance scores that gauge building sustainability.
【Keywords】: Splines; Green Buildings; Scoring; Interpolation
【Paper Link】 【Pages】:451-457
【Authors】: Valentin Robu ; Meritxell Vinyals ; Alex Rogers ; Nicholas R. Jennings
【Abstract】: Current electricity tariffs do not reflect the real cost that customers incur to suppliers, as units are charged at the same rate, regardless of how predictable each customer's consumption is. A recent proposal to address this problem are prediction-of-use tariffs. In such tariffs, a customer is asked in advance to predict her future consumption, and is charged based both on her actual consumption and the deviation from her prediction. Prior work {aamas2014} studied the cost game induced by a single such tariff, and showed customers would have an incentive to minimize their risk, by joining together when buying electricity as a grand coalition. In this work we study the efficient (i.e. cost-minimizing) structure of buying groups for the more realistic setting when multiple, competing prediction-of-use tariffs are available. We propose a polynomial time algorithm to compute efficient buyer groups, and validate our approach experimentally, using a large-scale data set of domestic electricity consumers in the UK.
【Keywords】: smart grids; electricity tariffs; group buying; coalition structure
【Paper Link】 【Pages】:458-464
【Authors】: Xuan Song ; Quanshi Zhang ; Yoshihide Sekimoto ; Ryosuke Shibasaki
【Abstract】: The frequency and intensity of natural disasters has significantly increased over the past decades and this trend is predicted to continue. Facing these possible and unexpected disasters, urban emergency management has become the especially important issue for the whole governments around the world.In this paper, we present a novel intelligent system for urban emergency management during the large-scale disasters. The proposed systemstores and manages the global positioning system (GPS) records from mobile devices used by approximately 1.6 million people throughout Japan over one year. By mining and analyzing population movements after the Great East Japan Earthquake, our system can automatically learn a probabilistic model to better understand and simulate human mobility during the emergency situations. Based on the learning model, population mobility in various urban areas impacted by the earthquake throughout Japan can be automatically simulated or predicted. On the basis of such kind of system, it is easy for us to find some new features or population mobility patterns after the recent and unprecedented composite disasters, which are likely to provide valuable experience and play a vital role for future disaster management worldwide.
【Keywords】: Human Mobililty; Urban Computing; Emergency Mangement
【Paper Link】 【Pages】:465-471
【Authors】: Daniel Urieli ; Peter Stone
【Abstract】: Sustainable energy systems of the future will no longer be able to rely on the current paradigm that energy supply follows demand. Many of the renewable energy resources do not produce power on demand, and therefore there is a need for new market structures that motivate sustainable behaviors by participants. The Power Trading Agent Competition (Power TAC) is a new annual competition that focuses on the design and operation of future retail power markets, specifically in smart grid environments with renewable energy production, smart metering, and autonomous agents acting on behalf of customers and retailers. It uses a rich, open-source simulation platform that is based on real-world data and state-of-the-art customer models. Its purpose is to help researchers understand the dynamics of customer and retailer decision-making, as well as the robustness of proposed market designs. This paper introduces TacTex'13, the champion agent from the inaugural competition in 2013. TacTex'13 learns and adapts to the environment in which it operates, by heavily relying on reinforcement learning and prediction methods. This paper describes the constituent components of TacTex'13 and examines its success through analysis of competition results and subsequent controlled experiments.
【Keywords】: Reinforcement Learning; Energy Trading; Smart-Grid; Machine Learning; Computational Sustainability; Sustainable Energy
【Paper Link】 【Pages】:472-478
【Authors】: Konstantina Valogianni ; Wolfgang Ketter ; John Collins ; Dmitry Zhdanov
【Abstract】: The growing Electric Vehicles' (EVs) popularity among commuters creates new challenges for the smart grid. The most important of them is the uncoordinated EV charging that substantially increases the energy demand peaks, putting the smart grid under constant strain. In order to cope with these peaks the grid needs extra infrastructure, a costly solution. We propose an Adaptive Management of EV Storage (AMEVS) algorithm, implemented through a learning agent that acts on behalf of individual EV owners and schedules EV charging over a weekly horizon. It accounts for individual preferences so that mobility service is not violated but also individual benefit is maximized. We observe that it reshapes the energy demand making it less volatile so that fewer resources are needed to cover peaks. It assumes Vehicle-to-Grid discharging when the customer has excess capacity. Our agent uses Reinforcement Learning trained on real world data to learn individual household consumption behavior and to schedule EV charging. Unlike previous work, AMEVS is a fully distributed approach. We show that AMEVS achieves significant reshaping of the energy demand curve and peak reduction, which is correlated with customer preferences regarding perceived utility of energy availability. Additionally, we show that the average and peak energy prices are reduced as a result of smarter energy use.
【Keywords】: Electric Vehicles;Smart Grid; Optimization; Reinforcement Learning
【Paper Link】 【Pages】:479-485
【Authors】: XiaoJian Wu ; Daniel Sheldon ; Shlomo Zilberstein
【Abstract】: We develop a fast approximation algorithm called rounded dynamic programming (RDP) for stochastic network design problems on directed trees. The underlying model describes phenomena that spread away from the root of a tree, for example, the spread of influence in a hierarchical organization or fish in a river network. Actions can be taken to intervene in the network—for some cost—to increase the probability of propagation along an edge. Our algorithm selects a set of actions to maximize the overall spread in the network under a limited budget. We prove that the algorithm is a fully polynomial-time approximation scheme (FPTAS), that is, it finds (1−ε)-optimal solutions in time polynomial in the input size and 1/ε. We apply the algorithm to the problem of allocating funds efficiently to remove barriers in a river network so fish can reach greater portions of their native range. Our experiments show that the algorithm is able to produce near-optimal solutions much faster than an existing technique.
【Keywords】:
【Paper Link】 【Pages】:486-492
【Authors】: Matt Wytock ; J. Zico Kolter
【Abstract】: We propose a new framework for single-channel source separation that liesbetween the fully supervised and unsupervised setting. Instead of supervision,we provide input features for each source signal and use convex methods toestimate the correlations between these features and the unobserved signaldecomposition. Contextually supervised source separation is a natural fit fordomains with large amounts of data but no explicit supervision; our motivatingapplication is energy disaggregation of hourly smart meter data (the separationof whole-home power signals into different energy uses). Here contextualsupervision allows us to provide itemized energy usage for thousands homes, a taskpreviously impossible due to the need for specialized data collection hardware.On smaller datasets which include labels, we demonstrate that contextualsupervision improves significantly over a reasonable baseline and existingunsupervised methods for source separation. Finally, we analyze the case of$\ell_2$ loss theoretically and show that recovery of the signal componentsdepends only on cross-correlation between features for different signals, not oncorrelations between features for the same signal.
【Keywords】:
【Paper Link】 【Pages】:493-499
【Authors】: Bo Yang ; Hua Guo ; Yi Yang ; Benyun Shi ; Xiao-Nong Zhou ; Jiming Liu
【Abstract】: Active surveillance is a desirable way to prevent the spread of infectious diseases in that it aims to timely discover individual incidences through an active searching for patients. However, in practice active surveillance is difficult to implement especially when monitoring space is large but available resources are limited. Therefore, it is extremely important for public health authorities to know how to distribute their very sparse resources to high-priority regions so as to maximize the outcomes of active surveillance. In this paper, we raise the problem of active surveillance planning and provide an effective method to address it via modeling and mining spatiotemporal patterns of infection risks from heterogeneous data sources. Taking malaria as an example, we perform an empirical study on real-world data to validate our method and provide our new findings.
【Keywords】: spatiotemporal data mining; heterogeneous data mining; active surveillance planning;
【Paper Link】 【Pages】:500-507
【Authors】: Jun Yu ; Rebecca A. Hutchinson ; Weng-Keen Wong
【Abstract】: Data quality is a common source of concern for large-scale citizen science projects like eBird. In the case of eBird, a major cause of poor quality data is the misidentification of bird species by inexperienced contributors. A proactive approach for improving data quality is to discover commonly misidentified bird species and to teach inexperienced birders the differences between these species. To accomplish this goal, we develop a latent variable graphical model that can identify groups of bird species that are often confused for each other by eBird participants. Our model is a multi-species extension of the classic occupancy-detection model in the ecology literature. This multi-species extension requires a structure learning step as well as a computationally expensive parameter learning stage which we make efficient through a variational approximation. We show that our model can not only discover groups of misidentified species, but by including these misidentifications in the model, it can also achieve more accurate predictions of both species occupancy and detection.
【Keywords】: Machine Learning; Probabilistic Graphical Model; Citizen Science; Crowdsourcing
【Paper Link】 【Pages】:508-514
【Authors】: Elliot Anshelevich ; Shreyas Sekar
【Abstract】: We study techniques to incentivize self-interested agents to form socially desirable solutions in scenarios where they benefit from mutual coordination. Towards this end, we consider coordination games where agents have different intrinsic preferences but they stand to gain if others choose the same strategy as them. For non-trivial versions of our game, stable solutions like Nash Equilibrium may not exist, or may be socially inefficient even when they do exist. This motivates us to focus on designing efficient algorithms to compute (almost) stable solutions like Approximate Equilibrium that can be realized if agents are provided some additional incentives. Our results apply in many settings like adoption of new products, project selection, and group formation, where a central authority can direct agents towards a strategy but agents may defect if they have better alternatives. We show that for any given instance, we can either compute a high quality approximate equilibrium or a near-optimal solution that can be stabilized by providing small payments to some players. Our results imply that a little influence is necessary in order to ensure that selfish players coordinate and form socially efficient solutions.
【Keywords】: Approximate Nash Equilibrium;Coordination Games;Price of Anarchy;Incentives
【Paper Link】 【Pages】:515-521
【Authors】: Javier Romero Davila ; Abdallah Saffidine ; Michael Thielscher
【Abstract】: The Game Description Language GDL is the standard input language for general game-playing systems. While players can gain a lot of traction by an efficient inference algorithm for GDL, state-of-the-art reasoners suffer from a variant of a classical KR problem, the inferential frame problem. We present a method by which general game players can transform any given game description into a representation that solves this problem. Our experimental results demonstrate that with the help of automatically generated domain knowledge, a significant speedup can thus be obtained for the majority of the game descriptions from the AAAI competition.
【Keywords】: General game playing, Inferential frame problem, Game description language
【Paper Link】 【Pages】:522-529
【Authors】: Sigal Sina ; Avi Rosenfeld ; Sarit Kraus
【Abstract】: Scenario-based serious-games have become an important tool for teaching new skills and capabilities. An important factor in the development of such systems is reducing the time and cost overheads in manually creating content for these scenarios. To address this challenge, we present ScenarioGen, an automatic method for generating content about everyday activities through combining computer science techniques with the crowd. ScenarioGen uses the crowd in three different ways: to capture a database of scenarios of everyday activities, to generate a database of likely replacements for specific events within that scenario, and to evaluate the resulting scenarios. We evaluated ScenarioGen in 6 different content domains and found that it was consistently rated as coherent and consistent as the originally captured content. We also compared ScenarioGen's content to that created by traditional planning techniques. We found that both methods were equally effective in generating coherent and consistent scenarios, yet ScenarioGen's content was found to be more varied and easier to create.
【Keywords】: Scenario-Based Serious-Games; Generated Content; Crowd-Sourcing
【Paper Link】 【Pages】:530-537
【Authors】: Alexander Zook ; Mark O. Riedl
【Abstract】: Game designs often center on the game mechanics - rules governing the logical evolution of the game. We seek to develop an intelligent system that generates computer games. As first steps towards this goal we present a composable and cross-domain representation for game mechanics that draws from AI planning action representations. We use a constraint solver to generate mechanics subject to design requirements on the form of those mechanics - what they do in the game. A planner takes a set of generated mechanics and tests whether those mechanics meet playability requirements - controlling how mechanics function in a game to affect player behavior. We demonstrate our system by modeling and generating mechanics in a role-playing game, platformer game, and combined role-playing-platformer game.
【Keywords】: games; procedural content generation
【Paper Link】 【Pages】:538-544
【Authors】: Colleen Alkalay-Houlihan ; Adrian Vetta
【Abstract】: Combinatorial auctions are multiple-item auctions in which bidders may place bids on any package (subset) of goods. This additional expressibility produces benefits that have led to combinatorial auctions becoming extremely important both in practice and in theory. In the computer science community, auction design has focused primarily on computational practicality and incentive compatibility. The latter concerns mechanisms that are resistant to bidders misrepresenting themselves via a single false identity; however, with modern forms of bid submission, such as electronic bidding, other types of cheating have become feasible. Prominent amongst them is false-name bidding; that is, bidding under pseudonyms. For example, the ubiquitous Vickrey-Clarke-Groves (VCG) mechanism is incentive compatible and produces optimal allocations, but it is not false-name-proof–bidders can increase their utility by submitting bids under multiple identifiers. Thus, there has recently been much interest in the design and analysis of false-name-proof auction mechanisms. These false-name-proof mechanisms, however, have polynomially small efficiency guarantees: they can produce allocations with very low economic efficiency/social welfare. In contrast, we show that, provided the degree to which different goods are complementary is bounded (as is the case in many important, practical auctions), the VCG mechanism gives a constant efficiency guarantee. Constant efficiency guarantees hold even at equilibria where the agents bid in a manner that is not individually rational. Thus, while an individual bidder may personally benefit greatly from making false-name bids, this will have only a small detrimental effect on the objective of the auctioneer: maximizing economic efficiency. So, from the auctioneer's viewpoint the VCG mechanism remains preferable to false-name-proof mechanisms.
【Keywords】: Auctions and Market-Based Systems; Mechanism Design; Equilibrium; E-Commerce
【Paper Link】 【Pages】:545-551
【Authors】: Haris Aziz ; Florian Brandl ; Felix Brandt
【Abstract】: Efficiency--no agent can be made better off without making another one worse off--and strategyproofness--no agent can obtain a more preferred outcome by misrepresenting his preferences--are two cornerstones of economics and ubiquitous in important areas such as voting, auctions, or matching markets. Within the context of random assignment, Bogomolnaia and Moulin have shown that two particular notions of efficiency and strategyproofness based on stochastic dominance are incompatible. However, there are various other possibilities of lifting preferences over alternatives to preferences over lotteries apart from stochastic dominance. In this paper, we give an overview of common preference extensions, propose two new ones, and show that the above-mentioned incompatibility can be extended to various other notions of strategyproofness and efficiency in randomized social choice.
【Keywords】: ordinal efficiency; strategyproofness; social decision schemes
【Paper Link】 【Pages】:552-558
【Authors】: Haris Aziz ; Serge Gaspers ; Simon Mackenzie ; Nicholas Mattei ; Paul Stursberg ; Toby Walsh
【Abstract】: Balanced knockout tournaments are one of the most common formats for sports competitions, and are also used in elections and decision-making. We consider the computational problem of finding the optimal draw for a particular player in such a tournament. The problem has generated considerable research within AI in recent years. We prove that checking whether there exists a draw in which a player wins is NP-complete, thereby settling an outstanding open problem. Our main result has a number of interesting implications on related counting and approximation problems. We present a memoization-based algorithm for the problem that is faster than previous approaches. Moreover, we highlight two natural cases that can be solved in polynomial time. All of our results also hold for the more general problem of counting the number of draws in which a given player is the winner.
【Keywords】: Knockout tournaments; Agenda Control
【Paper Link】 【Pages】:559-565
【Authors】: Haris Aziz ; Paul Stursberg
【Abstract】: The probabilistic serial rule is one of the most well-established and desirable rules for the random assignment problem. We present the egalitarian simultaneous reservation social decision scheme – an extension of probabilistic serial to the more general setting of randomized social choice. We consider various desirable fairness, efficiency, and strategic properties of social decision schemes and show that egalitarian simultaneous reservation compares favorably against existing rules. Finally, we define a more general class of social decision schemes called simultaneous reservation, that contains egalitarian simultaneous reservation as well as the serial dictatorship rules. We show that outcomes of simultaneous reservation characterize efficiency with respect to a natural refinement of stochastic dominance.
【Keywords】:
【Paper Link】 【Pages】:566-572
【Authors】: Eric Balkanski ; Simina Brânzei ; David Kurokawa ; Ariel D. Procaccia
【Abstract】: We introduce the simultaneous model for cake cutting (the fair allocation of a divisible good), in which agents simultaneously send messages containing a sketch of their preferences over the cake. We show that this model enables the computation of divisions that satisfy proportionality -- a popular fairness notion -- using a protocol that circumvents a standard lower bound via parallel information elicitation. Cake divisions satisfying another prominent fairness notion, envy-freeness, are impossible to compute in the simultaneous model, but admit arbitrarily good approximations.
【Keywords】: cake cutting; fair division; communication complexity; mechanism design
【Paper Link】 【Pages】:573-579
【Authors】: Avrim Blum ; Nika Haghtalab ; Ariel D. Procaccia
【Abstract】: Most work building on the Stackelberg security games model assumes that the attacker can perfectly observe the defender's randomized assignment of resources to targets. This assumption has been challenged by recent papers, which designed tailor-made algorithms that compute optimal defender strategies for security games with limited surveillance. We analytically demonstrate that in zero-sum security games, lazy defenders, who simply keep optimizing against perfectly informed attackers, are almost optimal against diligent attackers, who go to the effort of gathering a reasonable number of observations. This result implies that, in some realistic situations, limited surveillance may not need to be explicitly addressed.
【Keywords】: Stackelberg security games; Approximation; Sampling
【Paper Link】 【Pages】:580-586
【Authors】: Felix Brandt ; Markus Brill ; Paul Harrenstein
【Abstract】: An important subclass of social choice functions, so-called majoritarian (or C1) functions, only take into account the pairwise majority relation between alternatives. In the absence of majority ties--e.g., when there is an odd number of agents with linear preferences--the majority relation is antisymmetric and complete and can thus conveniently be represented by a tournament. Tournaments have a rich mathematical theory and many formal results for majoritarian functions assume that the majority relation constitutes a tournament. Moreover, most majoritarian functions have only been defined for tournaments and allow for a variety of generalizations to unrestricted preference profiles, none of which can be seen as the unequivocal extension of the original function. In this paper, we argue that restricting attention to tournaments is justified by the existence of a conservative extension, which inherits most of the commonly considered properties from its underlying tournament solution.
【Keywords】: Social choice theory; tournament solutions
【Paper Link】 【Pages】:587-593
【Authors】: Simina Brânzei ; Yiling Chen ; Xiaotie Deng ; Aris Filos-Ratsikas ; Søren Kristoffer Stiil Frederiksen ; Jie Zhang
【Abstract】: The Fisher market model is one of the most fundamental resource allocation models in economics. In a Fisher market, the prices and allocations of goods are determined according to the preferences and budgets of buyers to clear the market. In a Fisher market game, however, buyers are strategic and report their preferences over goods; the market-clearing prices and allocations are then determined based on their reported preferences rather than their real preferences. We show that the Fisher market game always has a pure Nash equilibrium, for buyers with linear, Leontief, and Cobb-Douglas utility functions, which are three representative classes of utility functions in the important Constant Elasticity of Substitution (CES) family. Furthermore, to quantify the social efficiency, we prove Price of Anarchy bounds for the game when the utility functions of buyers fall into these three classes respectively.
【Keywords】: Fisher market, Nash equilibrium, social welfare, Price of Anarchy
【Paper Link】 【Pages】:594-601
【Authors】: Noam Brown ; Tuomas Sandholm
【Abstract】: Regret matching is a widely-used algorithm for learning how to act. We begin by proving that regrets on actions in one setting (game) can be transferred to warm start the regrets for solving a different setting with same structure but different payoffs that can be written as a function of parameters. We prove how this can be done by carefully discounting the prior regrets. This provides, to our knowledge, the first principled warm-starting method for no-regret learning. It also extends to warm-starting the widely-adopted counterfactual regret minimization (CFR) algorithm for large incomplete-information games; we show this experimentally as well. We then study optimizing a parameter vector for a player in a two-player zero-sum game (e.g., optimizing bet sizes to use in poker). We propose a custom gradient descent algorithm that provably finds a locally optimal parameter vector while leveraging our warm-start theory to significantly save regret-matching iterations at each step. It optimizes the parameter vector while simultaneously finding an equilibrium. We present experiments in no-limit Leduc Hold'em and no-limit Texas Hold'em to optimize bet sizing. This amounts to the first action abstraction algorithm (algorithm for selecting a small number of discrete actions to use from a continuum of actions---a key preprocessing step for solving large games using current equilibrium-finding algorithms) with convergence guarantees for extensive-form games.
【Keywords】: Incomplete-Information Games; Poker; Game Solving; No-Regret Learning; Counterfactual Regret Minimization; Regret Matching; Regret Minimization
【Paper Link】 【Pages】:602-608
【Authors】: Neil Burch ; Michael Johanson ; Michael Bowling
【Abstract】: Decomposition, i.e. independently analyzing possible subgames, has proven to be an essential principle for effective decision-making in perfect information games. However, in imperfect information games, decomposition has proven to be problematic. To date, all proposed techniques for decomposition in imperfect information games have abandoned theoretical guarantees. This work presents the first technique for decomposing an imperfect information game into subgames that can be solved independently, while retaining optimality guarantees on the full-game solution. We can use this technique to construct theoretically justified algorithms that make better use of information available at run-time, overcome memory or disk limitations at run-time, or make a time/space trade-off to overcome memory or disk limitations while solving a game. In particular, we present an algorithm for subgame solving which guarantees performance in the whole game, in contrast to existing methods which may have unbounded error. In addition, we present an offline game solving algorithm, CFR-D, which can produce a Nash equilibrium for a game that is larger than available storage.
【Keywords】:
【Paper Link】 【Pages】:609-615
【Authors】: Ioannis Caragiannis ; David Kurokawa ; Ariel D. Procaccia
【Abstract】: We present a novel extension of normal form games that we call biased games. In these games, a player's utility is influenced by the distance between his mixed strategy and a given base strategy. We argue that biased games capture important aspects of the interaction between software agents. Our main result is that biased games satisfying certain mild conditions always admit an equilibrium. We also tackle the computation of equilibria in biased games.
【Keywords】: game theory; Nash equilibrium; normal-form games; biased games
【Paper Link】 【Pages】:616-622
【Authors】: Ioannis Caragiannis ; Ariel D. Procaccia ; Nisarg Shah
【Abstract】: Motivated by applications to crowdsourcing, we study voting rules that output a correct ranking of alternatives by quality from a large collection of noisy input rankings. We seek voting rules that are supremely robust to noise, in the sense of being correct in the face of any "reasonable" type of noise. We show that there is such a voting rule, which we call the modal ranking rule. Moreover, we establish that the modal ranking rule is the unique rule with the preceding robustness property within a large family of voting rules, which includes a slew of well-studied rules.
【Keywords】:
【Paper Link】 【Pages】:623-629
【Authors】: Vincent Conitzer ; Angelina Vidali
【Abstract】: We study the problem where a task (or multiple unrelated tasks) must be executed, there are multiple machines/agents that can potentially perform the task, and our objective is to minimize the expected sum of the agents' processing times. Each agent does not know exactly how long it will take him to finish the task; he only knows the distribution from which this time is drawn. These times are independent across agents and the distributions fulfill the monotone hazard rate condition. Agents are selfish and will lie about their distributions if this increases their expected utility. We study different variations of the Vickrey mechanism that take as input the agents' reported distributions and the players' realized running times and that output a schedule that minimizes the expected sum of processing times, as well as payments that make it an ex-post equilibrium for the agents to both truthfully report their distributions and exert full effort to complete the task. We devise the ChPE mechanism, which is uniquely tailored to our problem, and has many desirable properties including: not rewarding agents that fail to finish the task and having non-negative payments.
【Keywords】: mechanism design; scheduling; monotone hazard rate distribution; Vickrey mechanism; ex-post truthful; execution uncertainty
【Paper Link】 【Pages】:630-636
【Authors】: Trevor Davis ; Neil Burch ; Michael Bowling
【Abstract】: Extensive-form games are a powerful tool for representing complex multi-agent interactions. Nash equilibrium strategies are commonly used as a solution concept for extensive-form games, but many games are too large for the computation of Nash equilibria to be tractable. In these large games, exploitability has traditionally been used to measure deviation from Nash equilibrium, and thus strategies are aimed to achieve minimal exploitability. However, while exploitability measures a strategy's worst-case performance, it fails to capture how likely that worst-case is to be observed in practice. In fact, empirical evidence has shown that a less exploitable strategy can perform worse than a more exploitable strategy in one-on-one play against a variety of opponents. In this work, we propose a class of response functions that can be used to measure the strength of a strategy. We prove that standard no-regret algorithms can be used to learn optimal strategies for a scenario where the opponent uses one of these response functions. We demonstrate the effectiveness of this technique in Leduc Hold'em against opponents that use the UCT Monte Carlo tree search algorithm.
【Keywords】: strategy evaluation; extensive-form games; adaptive opponents
【Paper Link】 【Pages】:637-644
【Authors】: Moez Draief ; Hoda Heidari ; Michael Kearns
【Abstract】: In this paper, we introduce and examine two new models for competitive contagion in networks, a game-theoretic generalization of the viral marketing problem. In our setting, firms compete to maximize their market share in a network of consumers whose adoption decisions are stochastically determined by the choices of their neighbors. Building on the switching-selecting framework introduced by Goyal and Kearns, we first introduce a new model in which the payoff to firms comprises not only the number of vertices who adopt their (competing) technologies, but also the network connectivity among those nodes. For a general class of stochastic dynamics driving the local adoption process, we derive upper bounds on (1) the (pure strategy) Price of Anarchy (PoA), which measures the inefficiency of resource use at equilibrium, and (2) the Budget Multiplier, which captures the extent to which the network amplifies the imbalances in the firms' initial budgets. These bounds depend on the firm budgets and the maximum degree of the network, but no other structural properties. In addition, we give general conditions under which the PoA and the Budget Multiplier can be unbounded. We also introduce a model in which budgeting decisions are endogenous, rather than externally given as is typical in the viral marketing problem. In this setting, the firms are allowed to choose the number of seeds to initially infect (at a fixed cost per seed), as well as which nodes to select as seeds. In sharp contrast to the results of Goyal and Kearns, we show that for almost any local adoption dynamics, there exists a family of graphs for which the PoA and Budget Multiplier are unbounded.
【Keywords】: Competitive Contagion, Price of Anarchy, Connectivity, Endogenous Budget Constraints
【Paper Link】 【Pages】:645-653
【Authors】: Joanna Drummond ; Craig Boutilier
【Abstract】: While stable matching problems are widely studied, little work has investigated schemes for effectively eliciting agent preferences using either preference (e.g., comparison) queries for interviews (to form such comparisons); and no work has addressed how to combine both. We develop a new model for representing and assessing agent preferences that accommodates both forms of information and (heuristically) minimizes the number of queries and interviews required to determine a stable matching. Our Refine-then-Interview (RtI) scheme uses coarse preference queries to refine knowledge of agent preferences and relies on interviews only to assess comparisons of relatively “close” options. Empirical results show that RtI compares favorably to a recent pure interview minimization algorithm, and that the number of interviews it requires is generally independent of the size of the market.
【Keywords】: Stable Matching; Preference Elicitation
【Paper Link】 【Pages】:654-660
【Authors】: Edith Elkind ; Piotr Faliszewski ; Piotr Skowron
【Abstract】: We investigate elections that are simultaneously single-peaked and single-crossing (SPSC). We show that the domain of 1-dimensional Euclidean elections (where voters and candidates are points on the real line, and each voter prefers the candidates that are close to her to the ones that are further away) is a proper subdomain of the SPSC domain, by constructing an election that is single-peaked and single-crossing, but not 1-Euclidean. We then establish a connection between narcissistic elections (where each candidate is ranked first by at least one voter), single-peaked elections and single-crossing elections, by showing that an election is SPSC if and only if it can be obtained from a narcissistic single-crossing election by deleting voters. We show two applications of our characterization.
【Keywords】: single-peaked preferences; single-crossing preferences; euclidean preferences
【Paper Link】 【Pages】:661-667
【Authors】: Edith Elkind ; Martin Lackner
【Abstract】: Structured preference domains, such as, for example, the domains of single-peaked and single-crossing preferences, are known to admit efficient algorithms for many problems in computational social choice. Some of these algorithms extend to preferences that are close to having the respective structural property, i.e., can be made to enjoy this property by performing minor changes to voters' preferences, such as deleting a small number of voters or candidates. However, it has recently been shown that finding the optimal number of voters or candidates to delete in order to achieve the desired structural property is NP-hard for many such domains. In this paper, we show that these problems admit efficient approximation algorithms. Our results apply to all domains that can be characterized in terms of forbidden configurations; this includes, in particular, single-peaked and single-crossing elections. For a large range of scenarios, our approximation results are optimal under a plausible complexity-theoretic assumption. We also provide parameterized complexity results for this class of problems.
【Keywords】: Algorithms; Computational Social Choice; Domain Restrictions; Preferences; Approximation Algorithms
【Paper Link】 【Pages】:668-674
【Authors】: Ulle Endriss ; Umberto Grandi
【Abstract】: In binary aggregation, each member of a group expresses yes/no choices regarding several correlated issues and we need to decide on a collective choice that accurately reflects the views of the group. A good collective choice will minimise the distance to each of the individual choices, but using such a distance-based aggregation rule is computationally intractable. Instead, we explore a class of low-complexity aggregation rules that select the most representative voter in any given situation and return that voter's choice as the outcome.
【Keywords】: Approximation; Judgment Aggregation
【Paper Link】 【Pages】:675-681
【Authors】: Rupert Freeman ; Markus Brill ; Vincent Conitzer
【Abstract】: Runoff voting rules such as single transferable vote (STV) and Baldwin's rule are of particular interest in computational social choice due to their recursive nature and hardness of manipulation, as well as in (human) practice because they are relatively easy to understand. However, they are not known for their compliance with desirable axiomatic properties, which we attempt to rectify here. We characterize runoff rules that are based on scoring rules using two axioms: a weakening of local independence of irrelevant alternatives and a variant of population-consistency. We then show, as our main technical result, that STV is the only runoff scoring rule satisfying an independence-of-clones property. Furthermore, we provide axiomatizations of Baldwin's rule and Coombs' rule.
【Keywords】: computational social choice, runoff scoring rules, independence of clones
【Paper Link】 【Pages】:682-690
【Authors】: Sam Ganzfried ; Tuomas Sandholm
【Abstract】: There is often a large disparity between the size of a game we wish to solve and the size of the largest instances solvable by the best algorithms; for example, a popular variant of poker has about $10^{165}$ nodes in its game tree, while the currently best approximate equilibrium-finding algorithms scale to games with around $10^{12}$ nodes. In order to approximate equilibrium strategies in these games, the leading approach is to create a sufficiently small strategic approximation of the full game, called an abstraction, and to solve that smaller game instead. The leading abstraction algorithm for imperfect-information games generates abstractions that have imperfect recall and are distribution aware, using $k$-means with the earth mover's distance metric to cluster similar states together. A distribution-aware abstraction groups states together at a given round if their full distributions over future strength are similar (as opposed to, for example, just the expectation of their strength). The leading algorithm considers distributions over future strength at the final round of the game. However, one might benefit by considering the trajectory of distributions over strength in all future rounds, not just the final round. An abstraction algorithm that takes all future rounds into account is called potential aware. We present the first algorithm for computing potential-aware imperfect-recall abstractions using earth mover's distance. Experiments on no-limit Texas Hold'em show that our algorithm improves performance over the previously best approach.
【Keywords】: game theory; game abstraction; game solving; imperfect information; poker
【Paper Link】 【Pages】:691-697
【Authors】: Nicola Gatti ; Marco Rocco ; Sofia Ceppi ; Enrico H. Gerding
【Abstract】: Mobile geo-location advertising, where mobile ads are targeted based on a user’s location, has been identified as a key growth factor for the mobile market. As with online advertising, a crucial ingredient for their success is the development of effective economic mechanisms. An important difference is that mobile ads are shown sequentially over time and information about the user can be learned based on their movements. Furthermore, ads need to be shown selectively to prevent ad fatigue. To this end, we introduce, for the first time, a user model and suitable economic mechanisms which take these factors into account. Specifically, we design two truthful mechanisms which produce an advertisement plan based on the user’s movements. One mechanism is allocatively efficient, but requires exponential compute time in the worst case. The other requires polynomial time, but is not allocatively efficient. Finally, we experimentally evaluate the trade off between compute time and efficiency of our mechanisms.
【Keywords】: Auctions; Computational Advertising; Mobile Advertising
【Paper Link】 【Pages】:698-704
【Authors】: Judy Goldsmith ; Jérôme Lang ; Nicholas Mattei ; Patrice Perny
【Abstract】: Positional scoring rules in voting compute the score of an alternative by summing the scores for the alternative induced by every vote. This summation principle ensures that all votes contribute equally to the score of an alternative. We relax this assumption and, instead, aggregate scores by taking into account the rank of a score in the ordered list of scores obtained from the votes. This defines a new family of voting rules, rank-dependent scoring rules (RDSRs), based on ordered weighted average (OWA) operators, which, include all scoring rules, and many others, most of which of new. We study some properties of these rules, and show, empirically, that certain RDSRs are less manipulable than Borda voting, across a variety of statistical cultures.
【Keywords】: voting; computational social choice; scoring rules; ordered weighted average
【Paper Link】 【Pages】:705-711
【Authors】: Mingyu Guo ; Argyrios Deligkas ; Rahul Savani
【Abstract】: The VCG mechanism is the standard method to incentivize bidders in combinatorial auctions to bid truthfully. Under the VCG mechanism, the auctioneer can sometimes increase revenue by “burning” items. We study this phenomenon in a setting where items are described by a number of attributes. The value of an attribute corresponds to a quality level, and bidders’ valuations are non-decreasing in the quality levels. In addition to burning items, we allow the auctioneer to present some of the attributes as lower quality than they actually are. We consider the following two revenue maximization problems under VCG: finding an optimal way to mark down items by reducing their quality levels, and finding an optimal set of items to burn. We study the effect of the following parameters on the computational complexity of these two problems: the number of attributes, the number of quality levels per attribute, and the complexity of the bidders’ valuation functions. Bidders have unit demand, so VCG’s outcome can be computed in polynomial time, and the valuation functions we consider are step functions that are non-decreasing with the quality levels. We prove that both problems are NP-hard even in the following three simple settings: a) four attributes, arbitrarily many quality levels per attribute, and single-step valuation functions, b) arbitrarily many attributes, two quality levels per attribute, and single-step valuation functions, and c) one attribute, arbitrarily many quality levels, and multi-step valuation functions. For the case where items have only one attribute, and every bidder has a single-step valuation (zero below some quality threshold), we show that both problems can be solved in polynomial-time using a dynamic programming approach. For this case, we also quantify how much better marking down is than item burning, and we compare the revenue of both approaches with computational experiments.
【Keywords】: mechanism design; marking down; item burning; revenue maximization; VCG
【Paper Link】 【Pages】:712-720
【Authors】: Edith Hemaspaandra ; Lane A. Hemaspaandra ; Henning Schnoor
【Abstract】: Scoring systems are an extremely important class of election systems. A length-m (so-called) scoring vector applies only to m-candidate elections. To handle general elections, one must use a family of vectors, one per length. The most elegant approach to making sure such families are "family-like'' is the recently introduced notion of (polynomial-time uniform) pure scoring rules, where each scoring vector is obtained from its precursor by adding one new coefficient. We obtain the first dichotomy theorem for pure scoring rules for a control problem. In particular, for constructive control by adding voters (CCAV), we show that CCAV is solvable in polynomial time for k-approval with k<=3, k-veto with k<=2, every pure scoring rule in which only the two top-rated candidates gain nonzero scores, and a particular rule that is a "hybrid" of 1-approval and 1-veto. For all other pure scoring rules, CCAV is NP-complete. We also investigate the descriptive richness of different models for defining pure scoring rules, proving how more rule-generation time gives more rules, proving that rationals give more rules than do the natural numbers, and proving that some restrictions previously thought to be "w.l.o.g." in fact do lose generality.
【Keywords】: voting systems; computational social choice; complexity; scoring rules; control of elections; dichotomy theorems
【Paper Link】 【Pages】:721-727
【Authors】: Shweta Jain ; Balakrishnan Narayanaswamy ; Y. Narahari
【Abstract】: Demand response is a critical part of renewable integration and energy cost reduction goals across the world. Motivated by the need to reduce costs arising from electricity shortage and renewable energy fluctuations, we propose a novel multiarmed bandit mechanism for demand response (MAB-MDR) which makes monetary offers to strategic consumers who have unknown response characteristics, to incetivize reduction in demand. Our work is inspired by a novel connection we make to crowdsourcing mechanisms. The proposed mechanism incorporates realistic features of the demand response problem including time varying and quadratic cost function. The mechanism marries auctions, that allow users to report their preferences, with online algorithms, that allow distribution companies to learn user-specific parameters. We show that MAB-MDR is dominant strategy incentive compatible, individually rational, and achieves sublinear regret. Such mechanisms can be effectively deployed in smart grids using new information and control architecture innovations and lead to welcome savings in energy costs.
【Keywords】: Multiarmed Bandits; Mechanism Design; Smart Grids
【Paper Link】 【Pages】:728-734
【Authors】: Jeremy Karp ; Aleksandr M. Kazachkov ; Ariel D. Procaccia
【Abstract】: We study the envy-free allocation of indivisible goods between two players. Our novel setting includes an option to sell each good for a fraction of the minimum value any player has for the good. To rigorously quantify the efficiency gain from selling, we reason about the price of envy-freeness of allocations of sellable goods — the ratio between the maximum social welfare and the social welfare of the best envy-free allocation. We show that envy-free allocations of sellable goods are significantly more efficient than their unsellable counterparts.
【Keywords】: Computational social choice; Fair division; Envy-free allocation
【Paper Link】 【Pages】:735-741
【Authors】: Willemien Kets ; David M. Pennock ; Rajiv Sethi ; Nisarg Shah
【Abstract】: We investigate the limiting behavior of trader wealth and prices in a simple prediction market with a finite set of participants having heterogeneous beliefs. Traders bet repeatedly on the outcome of a binary event with fixed Bernoulli success probability. A class of strategies, including (fractional) Kelly betting and constant relative risk aversion (CRRA) are considered. We show that when traders are willing to risk only a small fraction of their wealth in any period, belief heterogeneity can persist indefinitely; if bets are large in proportion to wealth then only the most accurate belief type survives. The market price is more accurate in the long run when traders with less accurate beliefs also survive. That is, the survival of traders with heterogeneous beliefs, some less accurate than others, allows the market price to better reflect the objective probability of the event in the long run.
【Keywords】:
【Paper Link】 【Pages】:742-748
【Authors】: Martin Lackner
【Abstract】: Incomplete preferences are likely to arise in real-world preference aggregation and voting systems. This paper deals with determining whether an incomplete preference profile is single-peaked. This is essential information since many intractable voting problems become tractable for single-peaked profiles. We prove that for incomplete profiles the problem of determining single-peakedness is NP-complete. Despite this computational hardness result, we find four polynomial-time algorithms for reasonably restricted settings.
【Keywords】: Algorithms; Computational Social Choice; Single-Peaked; Preferences
【Paper Link】 【Pages】:749-755
【Authors】: Evangelos Markakis ; Orestis Telelis
【Abstract】: We present and analyze a mechanism for the Combinatorial Public Project Problem (CPPP). The problem asks to select k out of m available items, so as to maximize the social welfare for autonomous agents with combinatorial preferences (valuation functions) over subsets of items. The CPPP constitutes an abstract model for decision making by autonomous agents and has been shown to present severe computational hardness, in the design of truthful approximation mechanisms. We study a non-truthful mechanism that is, however, practically relevant to multi-agent environments, by virtue of its natural simplicity. It employs an Item Bidding interface, wherein every agent issues a separate bid for the inclusion of each distinct item in the outcome; the k items with the highest sums of bids are chosen and agents are charged according to a VCG-based payment rule. For fairly expressive classes of the agents' valuation functions, we establish existence of socially optimal pure Nash and strong equilibria, that are resilient to coordinated deviations of subsets of agents. Subsequently we derive tight worst-case bounds on the approximation of the optimum social welfare achieved in equilibrium. We show that the mechanism's performance improves with the number of agents that can coordinate, and reaches half of the optimum welfare at strong equilibrium.
【Keywords】: Mechanism; Social Welfare; Nash Equilibrium; Valuation Function
【Paper Link】 【Pages】:756-762
【Authors】: Thanh Hong Nguyen ; Amulya Yadav ; Bo An ; Milind Tambe ; Craig Boutilier
【Abstract】: Stackelberg security games (SSGs) have been deployed in a number of real-world domains. One key challenge in these applications is the assessment of attacker payoffs, which may not be perfectly known. Previous work has studied SSGs with uncertain payoffs modeled by interval uncertainty and provided maximin-based robust solutions. In contrast, in this work we propose the use of the less conservative minimax regret decision criterion for such payoff-uncertain SSGs and present the first algorithms for computing minimax regret for SSGs. We also address the challenge of preference elicitation, using minimax regret to develop the first elicitation strategies for SSGs. Experimental results validate the effectiveness of our approaches.
【Keywords】: security game; minimax regret; uncertainty; preference elicitation
【Paper Link】 【Pages】:763-769
【Authors】: Ariel D. Procaccia ; Nisarg Shah ; Max Lee Tucker
【Abstract】: We investigate synergy, or lack thereof, between agents in cooperative games, building on the popular notion of Shapley value. We think of a pair of agents as synergistic (resp., antagonistic) if the Shapley value of one agent when the other agent participates in a joint effort is higher (resp. lower) than when the other agent does not participate. Our main theoretical result is that any graph specifying synergistic and antagonistic pairs can arise even from a restricted class of cooperative games. We also study the computational complexity of determining whether a given pair of agents is synergistic. Finally, we use the concepts developed in the paper to uncover the structure of synergies in two real-world organizations, the European Union and the International Monetary Fund.
【Keywords】:
【Paper Link】 【Pages】:770-776
【Authors】: Goran Radanovic ; Boi Faltings
【Abstract】: We consider settings where a collective intelligence is formed by aggregating information contributed from many independent agents, such as product reviews, community sensing, or opinion polls. We propose a novel mechanism that elicits both private signals and beliefs. The mechanism extends the previous versions of the Bayesian Truth Serum (the original BTS, the RBTS, and the multi-valued BTS), by allowing small populations and non-binary private signals, while not requiring additional assumptions on the belief updating process. For priors that are sufficiently smooth, such as Gaussians, the mechanism allows signals to be continuous.
【Keywords】: Mechanism Design; Information Elicitation; Peer Prediction
【Paper Link】 【Pages】:777-783
【Authors】: Sudip Saha ; Abhijin Adiga ; Anil Kumar S. Vullikanti
【Abstract】: The spread of epidemics and malware is commonly modeled by diffusion processes on networks. Protective interventions such as vaccinations or installing anti-virus software are used to contain their spread. Typically, each node in the network has to decide its own strategy of securing itself, and its benefit depends on which other nodes are secure, making this a natural game-theoretic setting. There has been a lot of work on network security game models, but most of the focus has been either on simplified epidemic models or homogeneous network structure. We develop a new formulation for an epidemic containment game, which relies on the characterization of the SIS model in terms of the spectral radius of the network. We show in this model that pure Nash equilibria (NE) always exist, and can be found by a best response strategy. We analyze the complexity of finding NE, and derive rigorous bounds on their costs and the Price of Anarchy or PoA (the ratio of the cost of the worst NE to the optimum social cost) in general graphs as well as in random graph models. In particular, for arbitrary power-law graphs with exponent $\beta>2$, we show that the PoA is bounded by $O(T^{2(\beta-1)})$, where $T=\gamma/\alpha$ is the ratio of the recovery rate to the transmission rate in the SIS model. We prove that this bound is tight up to a constant factor for the Chung-Lu random power-law graph model. We study the characteristics of Nash equilibria empirically in different real communication and infrastructure networks, and find that our analytical results can help explain some of the empirical observations.
【Keywords】: Network Security Game; Nash Equilibria; Malware Propagation; Epidemic Control; Security; Protection; Game Theory; Graph Theory; Spectral Radius
【Paper Link】 【Pages】:784-790
【Authors】: Martin Schmid ; Matej Moravcik ; Milan Hladík
【Abstract】: It is a well known fact that in extensive form games with perfect information, there is a Nash equilibrium with support of size one. This doesn't hold for games with imperfect information, where the size of minimal support can be larger. We present a dependency between the level of uncertainty and the minimum support size. For many games, there is a big disproportion between the game uncertainty and the number of actions available. In Bayesian extensive games with perfect information, the only uncertainty is about the type of players. In card games, the uncertainty comes from dealing the deck. In these games, we can significantly reduce the support size. Our result applies to general-sum extensive form games with any finite number of players.
【Keywords】: Game theory, Nash equilibrium, Support, Poker
【Paper Link】 【Pages】:791-797
【Authors】: Akihisa Sonoda ; Etsushi Fujita ; Taiki Todo ; Makoto Yokoo
【Abstract】: Individual rationality, Pareto efficiency, and strategy- proofness are crucial properties of decision making functions, or mechanisms, in social choice literatures. In this paper we investigate mechanisms for exchange models where each agent is initially endowed with a set of goods and may have indifferences on distinct bundles of goods, and monetary transfers are not allowed. Sonmez (1999) showed that in such models, those three properties are not compatible in general. The impossibility, however, only holds under an assumption on preference domains. The main purpose of this paper is to discuss the compatibility of those three properties when the assumption does not hold. We first establish a preference domain called top-only preferences, which violates the assumption, and develop a class of exchange mechanisms that satisfy all those properties. Each mechanism in the class utilizes one instance of the mechanisms introduced by Saban and Sethuraman (2013). We also find a class of preference domains called m-chotomous preferences, where the assumption fails and these properties are incompatible.
【Keywords】:
【Paper Link】 【Pages】:798-804
【Authors】: Troels Bjerre Sørensen ; Melissa Dalis ; Joshua Letchford ; Dmytro Korzhyk ; Vincent Conitzer
【Abstract】: Gambles in casinos are usually set up so that the casino makes a profit in expectation -- as long as gamblers play honestly. However, some gamblers are able to cheat, reducing the casino’s profit. How should the casino address this? A common strategy is to selectively kick gamblers out, possibly even without being sure that they were cheating. In this paper, we address the following question: Based solely on a gambler’s track record,when is it optimal for the casino to kick the gambler out? Because cheaters will adapt to the casino’s policy, this is a game-theoretic question. Specifically, we model the problem as a Bayesian game in which the casino is a Stackelberg leader that can commit to a (possibly randomized) policy for when to kick gamblers out, and we provide efficient algorithms for computing the optimal policy. Besides being potentially useful to casinos, we imagine that similar techniques could be useful for addressing related problems -- for example, illegal trades in financial markets.
【Keywords】: Cheating; Gambling; Stackelberg; Security
【Paper Link】 【Pages】:805-811
【Authors】: Taiki Todo ; Haixin Sun ; Makoto Yokoo
【Abstract】: We study a mechanism design problem for exchange economies where each agent is initially endowed with a set of indivisible goods and side payments are not allowed. We assume each agent can withhold some endowments, as well as misreport her preference. Under this assumption, strategyproofness requires that for each agent, reporting her true preference with revealing all her endowments is a dominant strategy, and thus implies individual rationality. Our objective in this paper is to analyze the effect of such private ownership in exchange economies with multiple endowments. As fundamental results, we first show that the revelation principle holds under a natural assumption and that strategyproofness and Pareto efficiency are incompatible even under the lexicographic preference domain. We then propose a class of exchange rules, each of which has a corresponding directed graph to prescribe possible trades, and provide necessary and sufficient conditions on the graph structure so that they satisfy strategyproofness.
【Keywords】:
【Paper Link】 【Pages】:812-818
【Authors】: Fan Wu ; Junming Liu ; Zhenzhe Zheng ; Guihai Chen
【Abstract】: Online mechanism design has been widely applied to various practical applications. However, designing a strategy-proof online mechanism is much more challenging than that in a static scenario due to short of knowledge of future information. In this paper, we investigate online auctions with time discounting values, in contrast to the flat values studied in most of existing work. We present a strategy-proof 2-competitive online auction mechanism despite of time discounting values. We also implement our design and compare it with off-line optimal solution. Our numerical results show that our design achieves good performance in terms of social welfare, revenue, average winning delay, and average valuation loss.
【Keywords】: Online auction
【Paper Link】 【Pages】:819-825
【Authors】: Yingce Xia ; Tao Qin ; Nenghai Yu ; Tie-Yan Liu
【Abstract】: We study the existence of pure Nash equilibrium (PNE) for the mechanisms used in Internet services (e.g., online reviews and question-answering websites) to incentivize users to generate high-quality content. Most existing work assumes that users are homogeneous and have the same ability. However, real-world users are heterogeneous and their abilities can be very different from each other due to their diversity in background, culture, and profession. In this work, we consider the following setting: (1) the users are heterogeneous and each of them has a private type indicating the best quality of the content he/she can generate; (2) all the users share a fixed total reward. With this setting, we study the existence of pure Nash equilibrium of several mechanisms composed by different allocation rules, action spaces, and information availability. We prove the existence of PNE for some mechanisms and the non-existence for some other mechanisms. We also discuss how to find a PNE (if exists) through either a constructive way or a search algorithm.
【Keywords】:
【Paper Link】 【Pages】:826-834
【Authors】: Yue Yin ; Bo An ; Manish Jain
【Abstract】: High profile large scale public events are attractive targets for terrorist attacks. The recent Boston Marathon bombings on April 15, 2013 have further emphasized the importance of protecting public events. The security challenge is exacerbated by the dynamic nature of such events: e.g., the impact of an attack at different locations changes over time as the Boston marathon participants and spectators move along the race track. In addition, the defender can relocate security resources among potential attack targets at any time and the attacker may act at any time during the event. This paper focuses on developing efficient patrolling algorithms for such dynamic domains with continuous strategy spaces for both the defender and the attacker. We aim at computing optimal pure defender strategies, since an attacker does not have an opportunity to learn and respond to mixed strategies due to the relative infrequency of such events. We propose SCOUT-A, which makes assumptions on relocation cost, exploits payoff representation and computes optimal solutions efficiently. We also propose SCOUT-C to compute the exact optimal defender strategy for general cases despite the continuous strategy spaces. SCOUT-C computes the optimal defender strategy by constructing an equivalent game with discrete defender strategy space, then solving the constructed game. Experimental results show that both SCOUT-A and SCOUT-C significantly outperform other existing strategies.
【Keywords】: Game theory, Security, Optimization
【Paper Link】 【Pages】:835-841
【Authors】: Fahiem Bacchus ; Jessica Davies ; Maria Tsimpoukelli ; George Katsirelos
【Abstract】: A number of problems involve managing a set of optional clauses. For example, the soft clauses in a MAXSAT formula are optional—they can be falsified for a cost. Similarly, when computing a Minimum Correction Set for an unsatisfiable formula, all clauses are optional—some can be falsified in order to satisfy the remaining. In both of these cases the task is to find a subset of the optional clauses that achieves some criteria, and whose removal leaves a satisfiable formula. Relaxation search is a simple method of using a standard SAT solver to solve this task. Relaxation search is easy to implement, sometimes requiring only a simple modification of the variable selection heuristic in the SAT solver; it offers considerable flexibility and control over the order in which subsets of optional clauses are examined; and it automatically exploits clause learning to exchange information between the two phases of finding a suitable subset of optional clauses and checking if their removal yields satisfiability. We demonstrate how relaxation search can be used to solve MAXSAT and to compute Minimum Correction Sets. In both cases relaxation search is able to achieve state-of-the-art performance and solve some instances other solvers are not able to solve.
【Keywords】: sat; maxsat; optimization; minimal correction sets
【Paper Link】 【Pages】:842-848
【Authors】: André A. Ciré ; Serdar Kadioglu ; Meinolf Sellmann
【Abstract】: We consider the problem of parallelizing restarted backtrack search. With few notable exceptions, most commercial and academic constraint programming solvers do not learn no-goods during search. Depending on the branching heuristics used, this means that there are little to no side-effects between restarts, making them an excellent target for parallelization. We develop a simple technique for parallelizing restarted search deterministically and demonstrate experimentally that we can achieve near-linear speed-ups in practice.
【Keywords】:
【Paper Link】 【Pages】:849-855
【Authors】: Stefano Ermon ; Carla P. Gomes ; Ashish Sabharwal ; Bart Selman
【Abstract】: Markov Chains are a fundamental tool for the analysis of real world phenomena and randomized algorithms. Given a graph with some specified sink nodes and an initial probability distribution,we consider the problem of designing an absorbing Markov Chain that minimizes the time required to reach a sink node, by selecting transition probabilities subject to some natural regularity constraints. By exploiting the Markovian structure, we obtain closed form expressions for the objective function as well as its gradient, which can be thus evaluated efficiently without any simulation of the underlying process and fed to a gradient-based optimization package. For the special case of designing reversible Markov Chains, we show that global optimum can be efficiently computed by exploiting convexity. We demonstrate how our method can be used for the evaluation and design of local search methods tailored for certain domains.
【Keywords】:
【Paper Link】 【Pages】:856-862
【Authors】: Matthew Hatem ; Wheeler Ruml
【Abstract】: It is commonly appreciated that solving search problems optimally can take too long. Bounded suboptimal search algorithms trade increased solution cost for reduced solving time. Explicit Estimation Search (EES) is a recent state-of-the-art algorithm specifically designed for bounded suboptimal search. Although it tends to expand fewer nodes than alternative algorithms, such as weighted A (WA), its per-node expansion overhead is higher, causing it to sometimes take longer. In this paper, we present simplified variants of EES (SEES) and an earlier algorithm, Aepsilon (SAepsilon), that use different implementations of the same motivating ideas to significantly reduce search overhead and implementation complexity. In an empirical evaluation, we find that SEES, like EES, outperforms classic bounded suboptimal search algorithms, such as WA, on domains tested where distance-to-go estimates enable better search guidance. We also confirm that, while SEES and SAepsilon expand roughly the same number of nodes as their progenitors, they solve problems significantly faster and are much easier to implement. This work widens the applicability of state-of the-art bounded suboptimal search by making it easier to deploy.
【Keywords】:
【Paper Link】 【Pages】:863-870
【Authors】: Seyed Mehran Kazemi ; David Poole
【Abstract】: Various representations and inference methods have been proposed for lifted probabilistic inference in relational models. Many of these methods choose an order to eliminate (or branch on) the parameterized random variables. Similar to such methods for non-relational probabilistic inference, the order of elimination has a significant role in the performance of the algorithms. Since finding the best order is NP-complete even for non-relational models, heuristics have been proposed to find good orderings in the non-relational models. In this paper, we show that these heuristics are inefficient for relational models, because they fail to consider the population sizes associated with logical variables. We extend existing heuristics for non-relational models and propose new heuristics for relational models. We evaluate the existing and new heuristics on a range of generated relational graphs.
【Keywords】: Probabilistic Graphical Modes; Probabilistic Relational Models; Statistical Relational AI; Lifted Inference; Elimination Ordering; Searched-based Lifted Inference; Elimination Ordering Heuristics
【Paper Link】 【Pages】:871-877
【Authors】: Guni Sharon ; Ariel Felner ; Nathan R. Sturtevant
【Abstract】: In the Real-Time Agent-Centered Search (RTACS) problem,an agent has to arrive at a goal location while acting and reasoningin the physical world. Traditionally, RTACS problemsare solved by propagating and updating heuristic values ofstates visited by the agent. In existing RTACS algorithms theagent may revisit each state many times causing the entireprocedure to be quadratic in the state space. We study theIterative Deepening (ID) approach for solving RTACS andintroduce Exponential Deepening A (EDA), an RTACS algorithmwhere the threshold between successive Depth-Firstcalls is increased exponentially. EDA* is proven to hold aworst case bound that is linear in the state space. Experimentalresults supporting this bound are presented and demonstrateup to 10x reduction over existing RTACS solvers wrtdistance traveled, states expanded and CPU runtime.
【Keywords】: Real-time; Agent-centered; Heuristic search
【Paper Link】 【Pages】:878-884
【Authors】: Tansel Uras ; Sven Koenig
【Abstract】: Search with Subgoal Graphs (Uras, Koenig, and Hernandez 2013) was a non-dominated optimal path-planning algorithm in the Grid-Based Path Planning Competitions 2012 and 2013. During a preprocessing phase, it computes a Simple Subgoal Graph from a given grid, which is analogous to a visibility graph for continuous terrain, and then partitions the vertices into global and local subgoals to obtain a Two-Level Subgoal Graph. During the path-planning phase, it performs an A* search that ignores local subgoals that are not relevant to the search, which significantly reduces the size of the graph being searched. In this paper, we generalize this partitioning process to any undirected graph and show that it can be recursively applied to generate more than two levels, which reduces the size of the graph being searched even further. We distinguish between basic partitioning, which only partitions the vertices into different levels, and advanced partitioning, which can also add new edges.We show that the construction of Simple-Subgoal Graphs from grids and the construction of Two-Level Subgoal Graphs from Simple Subgoal Graphs are instances of generalized partitioning. We then report on experiments on Subgoal Graphs that demonstrate the effects of different types and levels of partitioning. We also report on experiments that demonstrate that our new N-Level Subgoal Graphs achieve a speed up of 1.6 compared to Two-Level Subgoal graphs from (Uras, Koenig, and Hern´andez 2013) on maps from the video games StarCraft and Dragon Age: Origins.
【Keywords】: Heuristic Search; Hierarchical Search; Subgoal Graph
【Paper Link】 【Pages】:885-892
【Authors】: Richard Anthony Valenzano ; Nathan R. Sturtevant ; Jonathan Schaeffer
【Abstract】: The use of inconsistent heuristics with A can result in increased runtime due to the need to re-expand nodes. Poor performance can also be seen with Weighted A if nodes are re-expanded. While the negative impact of re-expansions can often be minimized by setting these algorithms to never expand nodes more than once, the result can be a lower solution quality. In this paper, we formally show that the loss in solution quality can be bounded based on the amount of inconsistency along optimal solution paths. This bound holds regardless of whether the heuristic is admissible or inadmissible, though if the heuristic is admissible the bound can be used to show that not re-expanding nodes can have at most a quadratic impact on the quality of solutions found when using A. We then show that the bound is tight by describing a process for the construction of graphs for which a best-first search that does not re-expand nodes will find solutions whose quality is arbitrarily close to that given by the bound. Finally, we will use the bound to extend a known result regarding the solution quality of WA when weighting a consistent heuristic, so that it also applies to other types of heuristic weighting.
【Keywords】: Heuristic search; inconsistent heuristic; node re-expansions; best-first search; inadmissible heuristic; not re-expanding
【Paper Link】 【Pages】:893-900
【Authors】: Weizhong Zhang ; Lijun Zhang ; Yao Hu ; Rong Jin ; Deng Cai ; Xiaofei He
【Abstract】: In this paper, we focus on Stochastic Composite Optimization (SCO) for sparse learning that aims to learn a sparse solution. Although many SCO algorithms have been developed for sparse learning with an optimal convergence rate $O(1/T)$, they often fail to deliver sparse solutions at the end either because of the limited sparsity regularization during stochastic optimization or due to the limitation in online-to-batch conversion. To improve the sparsity of solutions obtained by SCO, we propose a simple but effective stochastic optimization scheme that adds a novel sparse online-to-batch conversion to the traditional SCO algorithms. The theoretical analysis shows that our scheme can find a solution with better sparse patterns without affecting the convergence rate. Experimental results on both synthetic and real-world data sets show that the proposed methods are more effective in recovering the sparse solution and have comparable convergence rate as the state-of-the-art SCO algorithms for sparse learning.
【Keywords】: Stochastic Optimization; Online Learning; Composite Gradient Mapping; Stochastic Gradient Descent
【Paper Link】 【Pages】:901-907
【Authors】: Marina Boia ; Claudiu Cristian Musat ; Boi Faltings
【Abstract】: Many Artificial Intelligence tasks need large amounts of commonsense knowledge. Because obtaining this knowledge through machine learning would require a huge amount of data, a better alternative is to elicit it from people through human computation. We consider the sentiment classification task, where knowledge about the contexts that impact word polarities is crucial, but hard to acquire from data. We describe a novel task design that allows us to crowdsource this knowledge through Amazon Mechanical Turk with high quality. We show that the commonsense knowledge acquired in this way dramatically improves the performance of established sentiment classification methods.
【Keywords】: human computation; games with a purpose; crowdsourcing; commonsense knowledge; sentiment analysis; context
【Paper Link】 【Pages】:908-915
【Authors】: Christopher H. Lin ; Ece Kamar ; Eric Horvitz
【Abstract】: We exploit the absence of signals as informative observations in the context of providing task recommendations in crowdsourcing. Workers on crowdsourcing platforms do not provide explicit ratings about tasks. We present methods that enable a system to leverage implicit signals about task preferences. These signals include types of tasks that have been available and have been displayed, and the number of tasks workers select and complete. In contrast to previous work, we present a general model that can represent both positive and negative implicit signals. We introduce algorithms that can learn these models without exceeding the computational complexity of existing approaches. Finally, using data from a high-throughput crowdsourcing platform, we show that reasoning about both positive and negative implicit feedback can improve the quality of task recommendations.
【Keywords】: crowdsourcing; implicit feedback; matrix factorization
【Paper Link】 【Pages】:916-922
【Authors】: Cody Buntain ; Amos Azaria ; Sarit Kraus
【Abstract】: This paper explores whether the addition of costly, imperfect, and exploitable advisors to Berg's investment game enhances or detracts from investor performance in both one-shot and multi-round interactions.We then leverage our findings to develop an automated investor agent that performs as well as or better than humans in these games.To gather this data, we extended Berg's game and conducted a series of experiments using Amazon's Mechanical Turk to determine how humans behave in these potentially adversarial conditions.Our results indicate that, in games of short duration, advisors do not stimulate positive behavior and are not useful in providing actionable advice.In long-term interactions, however, advisors do stimulate positive behavior with significantly increased investments and returns.By modeling human behavior across several hundred participants, we were then able to develop agent strategies that maximized return on investment and performed as well as or significantly better than humans.In one-shot games, we identified an ideal investment value that, on average, resulted in positive returns as long as advisor exploitation was not allowed.For the multi-round games, our agents relied on the corrective presence of advisors to stimulate positive returns on maximum investment.
【Keywords】: trust; advisor agent; investment game
【Paper Link】 【Pages】:923-929
【Authors】: Avshalom Elmalech ; David Sarne ; Noa Agmon
【Abstract】: Peer Designed Agents (PDAs), computer agents developed by non-experts, is an emerging technology, widely advocated in recent literature for the purpose of replacing people in simulations and investigating human behavior. Its main premise is that strategies programmed into these agents reliably reflect, to some extent, the behavior used by their programmers in real life. In this paper we show that PDA development has an important side effect that has not been addressed to date -- the process that merely attempts to capture one's strategy is also likely to affect the developer's strategy. The phenomenon is demonstrated experimentally, using several performance measures. This result has many implications concerning the appropriate design of PDA-based simulations, and the validity of using PDAs for studying individual decision making. Furthermore, we obtain that PDA development actually improved the developer's strategy according to all performance measures. Therefore, PDA development can be suggested as a means for improving people's problem solving skills.
【Keywords】: Decision-making; Peer Designed Agents; The Doors Game; Simulating Humans
【Paper Link】 【Pages】:930-936
【Authors】: Chen Hajaj ; Noam Hazon ; David Sarne
【Abstract】: The popularity of online shopping has contributed to the development of comparison shopping agents (CSAs) aiming to facilitate buyers' ability to compare prices of online stores for any desired product. Furthermore, the plethora of CSAs in today's markets enables buyers to query more than a single CSA when shopping, thus expanding even further the list of sellers whose prices they obtain. This potentially decreases the chance of a purchase based on the prices outputted as a result of any single query, and consequently decreases each CSAs' expected revenue per-query. Obviously, a CSA can improve its competence in such settings by acquiring more sellers' prices, potentially resulting in a more attractive ``best price''. In this paper we suggest a complementary approach that improves the attractiveness of a CSA by presenting the prices to the user in a specific intelligent manner, which is based on known cognitive-biases.The advantage of this approach is its ability to affect the buyer's tendency to terminate her search for a better price, hence avoid querying further CSAs, without having the CSA spend any of its resources on finding better prices to present.The effectiveness of our method is demonstrated using real data, collected from four CSAs for five products. Our experiments with people confirm that the suggested method effectively influence people in a way that is highly advantageous to the CSA.
【Keywords】: comparison shopping agents; belief-adjustment; ordering; experimentation; eCommerce
【Paper Link】 【Pages】:937-943
【Authors】: Robert Tyler Loftin ; James MacGlashan ; Bei Peng ; Matthew E. Taylor ; Michael L. Littman ; Jeff Huang ; David L. Roberts
【Abstract】: This paper introduces two novel algorithms for learning behaviors from human-provided rewards. The primary novelty of these algorithms is that instead of treating the feedback as a numeric reward signal, they interpret feedback as a form of discrete communication that depends on both the behavior the trainer is trying to teach and the teaching strategy used by the trainer. For example, some human trainers use a lack of feedback to indicate whether actions are correct or incorrect, and interpreting this lack of feedback accurately can significantly improve learning speed. Results from user studies show that humans use a variety of training strategies in practice and both algorithms can learn a contextual bandit task faster than algorithms that treat the feedback as numeric. Simulated trainers are also employed to evaluate the algorithms in both contextual bandit and sequential decision-making tasks with similar results.
【Keywords】: learning from feedback; machine learning; reinforcement learning; interactive learning; learning from demonstration; dog training; bayesian inference; expectation maximization
【Paper Link】 【Pages】:944-950
【Authors】: Brian O'Neill ; Mark Riedl
【Abstract】: We introduce Dramatis, a computational model of suspense based on a reformulation of a psychological definition of the suspense phenomenon. In this reformulation, suspense is correlated with the audience’s ability to generate a plan for the protagonist to avoid an impending negative outcome. Dramatis measures the suspense level by generating such a plan and determining its perceived likelihood of success. We report on three evaluations of Dramatis, including a comparison of Dramatis output to the suspense reported by human readers, as well as ablative tests of Dramatis components. In these studies, we found that Dramatis output corresponded to the suspense ratings given by human readers for stories in three separate domains.
【Keywords】: narrative; suspense; computational creativity; affective computing
【Paper Link】 【Pages】:951-957
【Authors】: Jie Wu ; Changhu Wang ; Liqing Zhang ; Yong Rui
【Abstract】: In this paper, we target at the problem of sketch recognition. We systematically study how to incorporate users' correction and editing into isolated and full sketch recognition. This is a natural and necessary interaction in real systems such as Visio where very similar shapes exist. First, a novel algorithm is proposed to mine the prior shape knowledge for three editing modes. Second, to differentiate visually similar shapes, a novel symbol recognition algorithm is introduced by leveraging the learnt shape knowledge. Then, a novel editing detection algorithm is proposed to facilitate symbol recognition. Furthermore, both of the symbol recognizer and the editing detector are systematically incorporated into the full sketch recognition. Finally, based on the proposed algorithms, a real-time sketch recognition system is built to recognize hand-drawn flowcharts and diagrams with flexible interactions. Extensive experiments show the effectiveness of the proposed algorithms.
【Keywords】: Sketch Recognition; Symbol Recognition; User Interface; Correction and Editing; Shape Knowledge
【Paper Link】 【Pages】:958-965
【Authors】: Jing Zhang ; Jie Tang ; Honglei Zhuang ; Cane Wing-Ki Leung ; Juan-Zi Li
【Abstract】: Conformity is the inclination of a person to be influenced by others. In this paper, we study how the conformity tendency of a person changes with her role, as defined by her structural properties in a social network. We first formalize conformity using a utility function based on the conformity theory from social psychology, and validate the proposed utility function by proving the existence of Nash Equilibria when all users in a network behave according to it. We then extend and incorporate the utility function into a probabilistic topic model, called the Role-Conformity Model (RCM), for modeling user behaviors under the effect of conformity. We apply the proposed RCM to several academic research networks, and discover that people with higher degree and lower clustering coefficient are more likely to conform to others. We also evaluate RCM through the task of word usage prediction in academic publications, and show significant improvements over baseline models.
【Keywords】: conformity; social influence
【Paper Link】 【Pages】:966-973
【Authors】: Shqiponja Ahmetaj ; Diego Calvanese ; Magdalena Ortiz ; Mantas Simkus
【Abstract】: In this paper we consider the setting of graph-structured data that evolves as a result of operations carried out by users or applications. We study different reasoning problems, which range from ensuring the satisfaction of a given set of integrity constraints after a given sequence of updates, to deciding the (non-)existence of a sequence of actions that would take the data to an (un)desirable state, starting either from a specific data instance or from an incomplete description of it. We consider a simple action language in which actions are finite sequences of insertions and deletions of nodes and labels, and use Description Logics for describing integrity constraints and (partial) states of the data. We then formalize the data management problems mentioned above as a static verification problem and several planning problems. We provide algorithms and tight complexity bounds for the formalized problems, both for an expressive DL and for a variant of DL-Lite.
【Keywords】:
【Paper Link】 【Pages】:974-980
【Authors】: Gadi Aleksandrowicz ; Hana Chockler ; Joseph Y. Halpern ; Alexander Ivrii
【Abstract】: Halpern and Pearl introduced a definition of actual causality; Eiter and Lukasiewicz showed that computing whether X = x is a cause of Y = y is NP-complete in binary models (where all variables can take on only two values) and \Sigma^P_2-complete in general models. In the final version of their paper, Halpern and Pearl slightly modified the definition of actual cause, in order to deal with problems pointed by Hopkins and Pearl. As we show, this modification has a nontrivial impact on the complexity of computing actual cause. To characterize the complexity, a new family D_k^P , k = 1,2,3,..., of complexity classes is introduced, which generalizes the class D^P introduced by Papadimitriou and Yannakakis (DP is just D^P_1). We show that the complexity of computing causality under the updated definition is D^P_2 -complete. Chockler and Halpern extended the definition of causality by introducing notions of responsibility and blame. The complexity of determining the degree of responsibility and blame using the original definition of causality was completely characterized. Again, we show that changing the definition of causality affects the complexity, and completely characterize it using the updated definition.
【Keywords】: Knowledge Representation and Reasoning; Causal Models; Structural Causality; Complexity; Strong Cause; Actual Cause; Polynomial Hierarchy
【Paper Link】 【Pages】:981-988
【Authors】: Saadat Anwar ; Chitta Baral
【Abstract】: Understanding biological pathways is an important activity in the biological domain for drug development. Due to the parallelism and complexity inherent in pathways, computer models that can answer queries about pathways are needed. A researcher may ask `what-if' questions comparing alternate scenarios, that require deeper understanding of the underlying model. In this paper, we present overview of such a system we developed and an English-like high level language to express pathways and queries. Our language is inspired by high level action and query languages and it uses Petri Net execution semantics.
【Keywords】: Knowledge Representation, Knowledge Representation Languages, Bioinformatics
【Paper Link】 【Pages】:989-995
【Authors】: Vaishak Belle ; Hector J. Levesque
【Abstract】: The area of cognitive robotics is often subject to the criticism that the proposals investigated in the literature are too far removed from the kind of continuous uncertainty and noise seen in actual real-world robotics. This paper proposes a new language and an implemented system, called PREGO, based on the situation calculus, that is able to reason effectively about degrees of belief against noisy sensors and effectors in continuous domains. It embodies the representational richness of conventional logic-based action languages, such as context-sensitive successor state axioms, but is still shown to be efficient using a number of empirical evaluations. We believe that PREGO is a powerful framework for exploring real-time reactivity and an interesting bridge between logic and probability for cognitive robotics applications.
【Keywords】: cognitive robotics; continuous uncertainty; logic and probability; reasoning about action; reasoning about knowledge
【Paper Link】 【Pages】:996-1002
【Authors】: Meghyn Bienvenu ; Camille Bourgaux ; François Goasdoué
【Abstract】: Recently several inconsistency-tolerant semantics have been introduced for querying inconsistent description logic knowledge bases. Most of these semantics rely on the notion of a repair, defined as an inclusion-maximal subset of the facts (ABox) which is consistent with the ontology (TBox). In this paper, we study variants of two popular inconsistency-tolerant semantics obtained by replacing classical repairs by various types of preferred repair. We analyze the complexity of query answering under the resulting semantics, focusing on the lightweight logic DL-Lite_R. Unsurprisingly, query answering is intractable in all cases, but we nonetheless identify one notion of preferred repair, based upon priority levels, whose data complexity is "only" coNP-complete. This leads us to propose an approach combining incomplete tractable methods with calls to a SAT solver. An experimental evaluation of the approach shows good scalability on realistic cases.
【Keywords】: inconsistency-tolerant query answering; complexity of query answering; DL-Lite; conjunctive queries
【Paper Link】 【Pages】:1003-1011
【Authors】: Diego Calvanese ; Wolfgang Fischl ; Reinhard Pichler ; Emanuel Sallinger ; Mantas Simkus
【Abstract】: Mapping relational data to RDF is an important task for the development of the Semantic Web. To this end, the W3C has recently released a Recommendation for the so-called direct mapping of relational data to RDF. In this work, we propose an enrichment of the direct mapping to make it more faithful by transferring also semantic information present in the relational schema from the relational world to the RDF world. We thus introduce expressive identification constraints to capture functional dependencies and define an RDF Normal Form, which precisely captures the classical Boyce-Codd Normal Form of relational schemas.
【Keywords】: identification constraints; functional dependencies; normal forms; RDFS; direct mapping
【Paper Link】 【Pages】:1012-1019
【Authors】: Jens Claßen ; Martin Liebenberg ; Gerhard Lakemeyer ; Benjamin Zarrieß
【Abstract】: The action programming language GOLOG has been found useful for the control of autonomous agents such as mobile robots. In scenarios like these, tasks are often open-ended so that the respective control programs are non-terminating. Before deploying such programs on a robot, it is often desirable to verify that they meet certain requirements. For this purpose, Claßen and Lakemeyer recently introduced algorithms for the verification of temporal properties of GOLOG programs. However, given the expressiveness of GOLOG, their verification procedures are not guaranteed to terminate. In this paper, we show how decidability can be obtained by suitably restricting the underlying base logic, the effect axioms for primitive actions, and the use of actions within GOLOG programs. Moreover, we show that dropping any of these restrictions immediately leads to undecidability of the verification problem.
【Keywords】: Situation Calculus; Golog; Verification; Decidability
【Paper Link】 【Pages】:1020-1026
【Authors】: Marco Console ; Maurizio Lenzerini
【Abstract】: Ontology-based data access (OBDA) is a new paradigm aiming at accessing and managing data by means of an ontology, i.e., a conceptual representation of the domain of interest in the underlying information system. In the last years, this new paradigm has been used for providing users with abstract (independent from technological and system-oriented aspects), effective, and reasoning-intensive mechanisms for querying the data residing at the information system sources. In this paper we argue that OBDA, besides querying data, provides the right principles for devising a formal approach to data quality. In particular, we concentrate on one of the most important dimensions considered both in the literature and in the practice of data quality, namely consistency. We define a general framework for data consistency in OBDA, and present algorithms and complexity analysis for several relevant tasks related to the problem of checking data quality under this dimension, both at the extensional level (content of the data sources), and at the intensional level (schema of the data sources).
【Keywords】: Knowledge Representation and Reasoning; Ontologies; Description Logics
【Paper Link】 【Pages】:1027-1033
【Authors】: Giuseppe De Giacomo ; Riccardo De Masellis ; Marco Montali
【Abstract】: In this paper we study when an LTL formula on finite traces (LTLf formula) is insensitive to infiniteness, that is, it can be correctly handled as a formula on infinite traces under the assumption that at a certain point the infinite trace starts repeating an end event forever, trivializing all other propositions to false. This intuition has been put forward and (wrongly) assumed to hold in general in the literature. We define a necessary and sufficient condition to characterize whether an LTLf formula is insensitive to infiniteness, which can be automatically checked by any LTL reasoner. Then, we show that typical LTLf specification patterns used in process and service modeling in CS, as well as trajectory constraints in Planning and transition-based LTLf specifications of action domains in KR, are indeed very often insensitive to infiniteness. This may help to explain why the assumption of interpreting LTL on finite and on infinite traces has been (wrongly) blurred. Possibly because of this blurring, virtually all literature detours to Buechi automata for constructing the NFA that accepts the traces satisfying an LTLf formula. As a further contribution, we give a simple direct algorithm for computing such NFA.
【Keywords】: Linear Temporal Logic on finite traces; LTL patterns; finite-state automata
【Paper Link】 【Pages】:1034-1040
【Authors】: Jianfeng Du ; Kewen Wang ; Yi-Dong Shen
【Abstract】: ABox abduction is an important reasoning mechanism for description logic ontologies. It computes all minimal explanations (sets of ABox assertions) whose appending to a consistent ontology enforces the entailment of an observation while keeps the ontology consistent. We focus on practical computation for a general problem of ABox abduction, called the query abduction problem, where an observation is a Boolean conjunctive query and the explanations may contain fresh individuals neither in the ontology nor in the observation. However, in this problem there can be infinitely many minimal explanations. Hence we first identify a class of TBoxes called first-order rewritable TBoxes. It guarantees the existence of finitely many minimal explanations and is sufficient for many ontology applications. To reduce the number of explanations that need to be computed, we introduce a special kind of minimal explanations called representative explanations from which all minimal explanations can be retrieved. We develop a tractable method (in data complexity) for computing all representative explanations in a consistent ontology. xperimental results demonstrate that the method is efficient and scalable for ontologies with large ABoxes.
【Keywords】: ABox abduction; abductive reasoning; query abduction problem; description logics; first-order rewritable
【Paper Link】 【Pages】:1041-1048
【Authors】: Thomas Eiter ; Michael Fink ; Christoph Redl ; Daria Stepanova
【Abstract】: Answer set programs (ASP) with external evaluations are a declarative means to capture advanced applications. However, their evaluation can be expensive due to external source accesses. In this paper we consider HEX-programs that provide external atoms as a bidirectional interface to external sources and present a novel evaluation method based on support sets, which informally are portions of the input to an external atom that will determine its output for any completion of the partial input. Support sets allow one to shortcut the external source access, which can be completely eliminated. This is particularly attractive if a compact representation of suitable support sets is efficiently constructible. We discuss some applications with this property, among them description logic programs over DL-Lite ontologies, and present experimental results showing that support sets can significantly improve efficiency.
【Keywords】: Answer Set Programming;External Sources;Description Logic Programs
【Paper Link】 【Pages】:1049-1055
【Authors】: Hélène Fargier ; Pierre Marquis ; Alexandre Niveau ; Nicolas Schmidt
【Abstract】: Valued decision diagrams (VDDs) are data structures that represent functions mapping variable-value assignments to non-negative real numbers. They prove useful to compile cost functions, utility functions, or probability distributions. While the complexity of some queries (notably optimization) and transformations (notably conditioning) on VDD languages has been known for some time, there remain many significant queries and transformations, such as the various kinds of cuts, marginalizations, and combinations, the complexity of which has not been identified so far. This paper contributes to filling this gap and completing previous results about the time and space efficiency of VDD languages, thus leading to a knowledge compilation map for real-valued functions. Our results show that many tasks that are hard on valued CSPs are actually tractable on VDDs.
【Keywords】: knowledge compilation;decision diagram;complexity;ADD;SLDD;AADD;
【Paper Link】 【Pages】:1056-1062
【Authors】: Benjamin J. Hescott ; Roni Khardon
【Abstract】: Recent work introduced Generalized First Order Decision Diagrams (GFODD) as a knowledge representation that is useful in mechanizing decision theoretic planning in relational domains. GFODDs generalize function-free first order logic and include numerical values and numerical generalizations of existential and universal quantification. Previous work presented heuristic inference algorithms for GFODDs. In this paper, we study the complexity of the evaluation problem, the satiability problem, and the equivalence problem for GFODDs under the assumption that the size of the intended model is given with the problem, a restriction that guarantees decidability. Our results provide a complete characterization. The same characterization applies to the corresponding restriction of problems in first order logic, giving an interesting new avenue for efficient inference when the number of objects is bounded. Our results show that for Σk formulas, and for corresponding GFODDs, evaluation and satisfiability are Σkp complete, and equivalence is Πk+1p complete. For Πk formulas evaluation is Πkp complete, satisfiability is one level higher and is Σk+1p complete, and equivalence is Πk+1p complete.
【Keywords】: Complexity Analysis; polynomial Hierarchy; Knowledge Representation
【Paper Link】 【Pages】:1063-1069
【Authors】: Jianmin Ji ; Hai Wan ; Peng Xiao ; Ziwei Huo ; Zhanhao Xiao
【Abstract】: The notions of loops and loop formulas play an important role in answer set computation. However, there would be an exponential number of loops in the worst case. Gebser and Schaub characterized a subclass elementary loops and showed that they are sufficient for selecting answer sets from models of a logic program. This paper proposes an alternative definition of elementary loops and identify a subclass of elementary loops, called proper loops. By applying a special form of their loop formulas, proper loops are also sufficient for the SAT-based answer set computation. A polynomial algorithm to recognize a proper loop is given and shows that for certain logic programs, identifying all proper loops of a program is more efficient than that of elementary loops. Furthermore, we prove that, by considering the structure of the positive body-head dependency graph of a program, a large number of loops could be ignored for identifying proper loops. We provide another algorithm for identifying all proper loops of a program. The experiments show that, for certain programs whose dependency graphs consisting of sets of components that are densely connected inside and sparsely connected outside, the new algorithm is more efficient.
【Keywords】: Answer Set Programming; Elementary Loops; Dependency Graphs
【Paper Link】 【Pages】:1070-1076
【Authors】: Souhila Kaci ; Yakoub Salhi
【Abstract】: Dung's argumentation framework is an abstract framework based on a set of arguments and a binary attack relation defined over the set. One instantiation, among many others, of Dung's framework consists in constructing the arguments from a set of propositional logic formulas. Thus an argument is seen as a reason for or against the truth of a particular statement. Despite its advantages, the argumentation approach for inconsistency handling also has important shortcomings. More precisely, in some applications what one is interested in are not so much only the conclusions supported by the arguments but also the precise explications of such conclusions. We show that argumentation framework applied to classical logic formulas is not suitable to deal with this problem. On the other hand, intuitionistic logic appears to be a natural alternative candidate logic (instead of classical logic) to instantiate Dung's framework. We develop constructive argumentation framework. We show that intuitionistic logic offers nice and desirable properties of the arguments. We also provide a characterization of the arguments in this setting in terms of minimal inconsistent subsets when intuitionistic logic is embedded in the modal logic S4.
【Keywords】: Intuitionistic Logic; Argumentation;
【Paper Link】 【Pages】:1077-1083
【Authors】: Mark Kaminski ; Yavor Nenov ; Bernardo Cuenca Grau
【Abstract】: We study the problem of rewriting a disjunctive datalog program into plain datalog. We show that a disjunctive program is rewritable if and only if it is equivalent to a linear disjunctive program, thus providing a novel characterisation of datalog rewritability. Motivated by this result, we propose weakly linear disjunctive datalog -- a novel rule-based KR language that extends both datalog and linear disjunctive datalog and for which reasoning is tractable in data complexity. We then explore applications of weakly linear programs to ontology reasoning and propose a tractable extension of OWL 2 RL with disjunctive axioms. Our empirical results suggest that many non-Horn ontologies can be reduced to weakly linear programs and that query answering over such ontologies using a datalog engine is feasible in practice.
【Keywords】: knowledge representation and reasoning; ontologies; datalog rewritability; disjunctive datalog; query answering; description logics; OWL 2
【Paper Link】 【Pages】:1084-1090
【Authors】: Matthew Evans Klenk ; Johan de Kleer ; Daniel G. Bobrow ; Bill Janssen
【Abstract】: Qualitative reasoning can play an important role in early stage design. Currently, engineers explore the design space using simulation models built in languages such as Modelica. To make qualitative reasoning useful to them, designs specified in their languages must be translated into a qualitative modeling language for analysis. The contribution of this paper is a sound and effective mapping between Modelica and qualitative reasoning. To achieve a sound mapping, we extend envisioning, the process of generating all relevant qualitative behaviors, to support Modelica's declarative events. For an effective mapping, we identify three classes of additional constraints that should be inferred from the Modelica representation thereby exponentially reducing the number of unrealizable trajectories. We support this contribution with examples and a case study.
【Keywords】: Qualitative Reasoning; AI and Design; Knowledge Representation
【Paper Link】 【Pages】:1091-1097
【Authors】: Martin Kronegger ; Martin Lackner ; Andreas Pfandler ; Reinhard Pichler
【Abstract】: Generalized CP-nets (GCP-nets) allow a succinct representation of preferences over multi-attribute domains. As a consequence of their succinct representation, many GCP-net related tasks are computationally hard. Even finding the more preferable of two outcomes is PSPACE-complete. In this work, we employ the framework of parameterized complexity to achieve two goals: First, we want to gain a deeper understanding of the complexity of GCP-nets. Second, we search for efficient fixed-parameter tractable algorithms.
【Keywords】: Computational social choice; CP-nets; Fixed-parameter tractable algorithms; (Parameterized) complexity theory
【Paper Link】 【Pages】:1098-1104
【Authors】: Takuya Matsuzaki ; Hidenao Iwane ; Hirokazu Anai ; Noriko H. Arai
【Abstract】: We report on a project aiming at developing a system that solves a wide range of math problems written in natural language. In the system, formal analysis of natural language semantics is coupled with automated reasoning technologies including computer algebra, using logic as their common language. We have developed a prototype system that accepts as its input a linguistically annotated problem text. Using the prototype system as a reference point, we analyzed real university entrance examination problems from the viewpoint of end-to-end automated reasoning. Further, evaluation on entrance exam mock tests revealed that an optimistic estimate of the system’s performance already matches human averages on a few test sets.
【Keywords】: Math problem solving; Automated reasoning; Natural language processing; formal semantics; Computer algebra; quantifier elimination; Logic
【Paper Link】 【Pages】:1105-1111
【Authors】: Hai Wan ; Zhanhao Xiao ; Zhenfeng Yuan ; Heng Zhang ; Yan Zhang
【Abstract】: This paper focuses on computing general first-order parallel and prioritized circumscription with varying constants. We propose linear translations from general first-order circumscription to first-order theories under stable model semantics over arbitrary structures, including Trv for parallel circumscription and Tr^s_v for conjunction of parallel circumscriptions (further for prioritized circumscription). To improve the efficiency, we give an optimization \Gamma{\exists} to reduce logic programs in size when eliminating existential quantifiers during the translations. Based on these results, a general first-order circumscription solver, named cfo2lp, is developed by calling answer set programming (ASP) solvers. Using circuit diagnosis problem and extended stable marriage problem as benchmarks, we compare cfo2lp with a propositional circumscription solver circ2dlp and an ASP solver with complex optimization metasp on efficiency. Experimental results demonstrate that for problems represented by first-order circumscription naturally and intuitively, cfo2lp can compute all solutions over finite structures. We also apply our approach to description logics with circumscription and repairs in inconsistent databases, which can be handled effectively.
【Keywords】: Parallel and Prioritized Circumscription; Stable Model Semantics; Solver
【Paper Link】 【Pages】:1112-1119
【Authors】: Zhen Wang ; Jianwen Zhang ; Jianlin Feng ; Zheng Chen
【Abstract】: We deal with embedding a large scale knowledge graph composed of entities and relations into a continuous vector space. TransE is a promising method proposed recently, which is very efficient while achieving state-of-the-art predictive performance. We discuss some mapping properties of relations which should be considered in embedding, such as reflexive, one-to-many, many-to-one, and many-to-many. We note that TransE does not do well in dealing with these properties. Some complex models are capable of preserving these mapping properties but sacrifice efficiency in the process. To make a good trade-off between model capacity and efficiency, in this paper we propose TransH which models a relation as a hyperplane together with a translation operation on it. In this way, we can well preserve the above mapping properties of relations with almost the same model complexity of TransE. Additionally, as a practical knowledge graph is often far from completed, how to construct negative examples to reduce false negative labels in training is very important. Utilizing the one-to-many/many-to-one mapping property of a relation, we propose a simple trick to reduce the possibility of false negative labeling. We conduct extensive experiments on link prediction, triplet classification and fact extraction on benchmark datasets like WordNet and Freebase. Experiments show TransH delivers significant improvements over TransE on predictive accuracy with comparable capability to scale up.
【Keywords】: Knowledge Embedding; Knowledge Graph; TransH
【Paper Link】 【Pages】:1120-1127
【Authors】: Fang Wei-Kleiner ; Zlatan Dragisic ; Patrick Lambrix
【Abstract】: In this paper we consider the problem of repairing missing is-a relations in ontologies. We formalize the problem as a generalized TBox abduction problem (GTAP). Based on this abduction framework, we present complexity results for the existence, relevance and necessity decision problems for the GTAP with and without some specific preference relations for ontologies that can be represented using a member of the EL family of description logics. Further, we present algorithms for finding solutions, a system as well as experiments.
【Keywords】: ontology engineering, ontology debugging, abduction
【Paper Link】 【Pages】:1128-1134
【Authors】: Michal Wrona
【Abstract】: Abduction is a form of nonmonotonic reasoning that looks for an explanation, built from a given set of hypotheses, for an observed manifestation according to some knowledge base. Following the concept behind the Schaefer's parametrization CSP(Gamma) of the Constraint Satisfaction Problem (CSP), we study here the complexity of the abduction problem Abduction(Gamma, Hyp, M) parametrized by certain (omega-categorical) infinite relational structures Gamma, Hyp, and M from which a knowledge base, hypotheses and a manifestation are built, respectively. We say that Gamma has local-to-global consistency if there is k such that establishing strong k-consistency on an instance of CSP(Gamma) yields a globally consistent (whose every solution may be obtained straightforwardly from partial solutions) set of constraints. In this case CSP(Gamma) is solvable in polynomial time. Our main contribution is an algorithm that under some natural conditions decides Abduction(Gamma, Hyp, M) in P when Gamma has local-to-global consistency. As we show in the number of examples, our approach offers an opportunity to consider abduction in the context of spatial and temporal reasoning (qualitative calculi such as Allen's interval algebra or RCC-5) and that our procedure solves some related abduction problems in polynomial time.
【Keywords】: Diagnosis and Abductive Reasoning, Spatial and Temporal Reasoning, Local Consistency, Computational Complexity
【Paper Link】 【Pages】:1135-1141
【Authors】: Tom Zamir ; Roni Tzvi Stern ; Meir Kalech
【Abstract】: We propose a combination of AI techniques to improve softwaretesting. When a test fails, a model-based diagnosis(MBD) algorithm is used to propose a set of possible explanations.We call these explanations diagnoses. Then, a planningalgorithm is used to suggest further tests to identify thecorrect diagnosis. A tester preforms these tests and reportstheir outcome back to the MBD algorithm, which uses thisinformation to prune incorrect diagnoses. This iterative processcontinues until the correct diagnosis is returned. We callthis testing paradigm Test, Diagnose and Plan (TDP). Severaltest planning algorithms are proposed to minimize the numberof TDP iterations, and consequently the number of testsrequired until the correct diagnosis is found. Experimentalresults show the benefits of using an MDP-based planning algorithmsover greedy test planning in three benchmarks.
【Keywords】: Model-based diagnosis, Planning under uncertainty, Testing
【Paper Link】 【Pages】:1142-1148
【Authors】: Yujiao Zhou ; Yavor Nenov ; Bernardo Cuenca Grau ; Ian Horrocks
【Abstract】: We present an enhanced hybrid approach to OWL query answering that combines an RDF triple-store with an OWL reasoner in order to provide scalable pay-as-you-go performance. The enhancements presented here include an extension to deal with arbitrary OWL ontologies, and optimisations that significantly improve scalability. We have implemented these techniques in a prototype system, a preliminary evaluation of which has produced very encouraging results.
【Keywords】: Ontologies; query answering; Datalog; Description Logic
【Paper Link】 【Pages】:1149-1156
【Authors】: Zhiqiang Zhuang ; Zhe Wang ; Kewen Wang ; Guilin Qi
【Abstract】: Two essential tasks in managing Description Logic (DL) ontologies are eliminating problematic axioms and incorporating newly formed axioms. Such elimination and incorporation are formalised as the operations of contraction and revision in belief change.In this paper, we deal with contraction and revision for the DL-Lite family through a model-theoretic approach.Standard DL semantics yields infinite numbers of models for DL-Lite TBoxes, thus it is not practical to develop algorithms for contraction and revision that involve DL models. The key to our approach is the introduction of an alternative semantics called type semantics which is more succinct than DL semantics. More importantly, with a finite signature, type semantics always yields finite humber of models.We then define model-based contraction and revision for DL-Lite TBoxesunder type semantics and provide representation theorems for them.Finally, the succinctness of type semantics allows us to develop tractable algorithms for both operations.
【Keywords】: Belief change
【Paper Link】 【Pages】:1157-1163
【Authors】: M. Hidayath Ansari ; Michael H. Coen ; Barbara B. Bendlin ; Mark A. Sager ; Sterling C. Johnson
【Abstract】: This paper introduces a novel framework for performing machine learning onlongitudinal neuroimaging datasets. These datasets are characterized by theirsize, particularly their width (millions of features per data input). Specifically, we address the problem of detecting subtle, short-term changes inneural structure that are indicative of cognitive change and correlate withrisk factors for Alzheimer's disease. We introduce a new spatially-sensitivekernel that allows us to reason about individuals, as opposed to populations. In doing so, this paper presents the first evidence demonstrating that verysmall changes in white matter structure over a two year period can predictchange in cognitive function in healthy adults.
【Keywords】: Alzheimer's disease; kernels; cognition; neuropsychological testing; spatially sensitive; DTI; APOE
【Paper Link】 【Pages】:1164-1170
【Authors】: Márcia Baptista ; Anjie Fang ; Helmut Prendinger ; Rui Prada ; Yohei Yamaguchi
【Abstract】: An important requirement of household energy simulation models is their accuracy in estimating energy demand and its fluctuations. Occupant behavior has a major impact upon energy demand. However, Markov chains, the traditional approach to model occupant behavior, (1) has limitations in accurately capturing the coordinated behavior of occupants and (2) is prone to over-fitting. To address these issues, we propose a novel approach that relies on a combination of data mining techniques. The core idea of our model is to determine the behavior of occupants based on nearest neighbor comparison over a database of sample data. Importantly, the model takes into account features related to the coordination of occupants' activities. We use a customized distance function suited for mixed categorical and numerical data. Further, association rule learning allows us to capture the coordination between occupants. Using real data from four households in Japan we are able to show that our model outperforms the traditional Markov chain model with respect to occupant coordination and generalization of behavior patterns.
【Keywords】: Occupant Behavior Models; Nearest Neighbor Search; Customized Distance Measure; Association Rule Learning;
【Paper Link】 【Pages】:1171-1177
【Authors】: Xiaojun Chang ; Feiping Nie ; Yi Yang ; Heng Huang
【Abstract】: Explosive growth of multimedia data has brought challenge of how to efficiently browse, retrieve and organize these data. Under this circumstance, different approaches have been proposed to facilitate multimedia analysis. Several semi-supervised feature selection algorithms have been proposed to exploit both labeled and unlabeled data. However, they are implemented based on graphs, such that they cannot handle large-scale datasets. How to conduct semi-supervised feature selection on large-scale datasets has become a challenging research problem. Moreover, existing multi-label feature selection algorithms rely on eigen-decomposition with heavy computational burden, which further prevent current feature selection algorithms from being applied for big data. In this paper, we propose a novel convex semi-supervised multi-label feature selection algorithm, which can be applied to large-scale datasets. We evaluate performance of the proposed algorithm over five benchmark datasets and compare the results with state-of-the-art supervised and semi-supervised feature selection algorithms as well as baseline using all features. The experimental results demonstrate that our proposed algorithm consistently achieve superiors performances.
【Keywords】: Semi-Supervised Feature Selection
【Paper Link】 【Pages】:1178-1184
【Authors】: Chih-Chun Chia ; James Blum ; Zahi N. Karam ; Satinder P. Singh ; Zeeshan Syed
【Abstract】: Postoperative atrial fibrillation (PAF) occurs in 10% to 65% of the patients undergoing cardiothoracic surgery. It is associated with increased post-surgical mortality and morbidity, and results in longer and more expensive hospital stays. Accurately stratifying patients for PAF allows for selective use of prophylactic therapies (e.g., amiodarone). Unfortunately, existing tools to stratify patients for PAF fail to provide clinically adequate discrimination. Our research addresses this situation through the development of novel electrocardiographic(ECG) markers to identify patients at risk of PAF. As a first step, we explore an eigen-decomposition approach that partitions ECG signals into atrial and ventricular components by exploiting knowledge of the underlying cardiac cycle. We then quantify electrical instability in the myocardium manifesting as probabilistic variations in atrial ECG morphology to assess therisk of PAF. When evaluated on 385 patients undergoing cardiac surgery, this approach of stratifying patients for PAF through an analysis of morphologic variability within decoupled atrial ECG demonstrated substantial promise and improved net reclassification by over 53% relative to the use of baseline clinical characteristics.
【Keywords】: atrial fibrillation; independent components; medicine
【Paper Link】 【Pages】:1185-1191
【Authors】: Puja Das ; Nicholas Johnson ; Arindam Banerjee
【Abstract】: In portfolio selection, it often might be preferable to focus on a few top performing industries/sectors to beat the market. These top performing sectors however might change over time. In this paper, we propose an online portfolio selection algorithm that can take advantage of sector information through the use of a group sparsity inducing regularizer while making lazy updates to the portfolio. The lazy updates prevent changing ones portfolio too often which otherwise might incur huge transaction costs. The proposed formulation leads to a non-smooth constrained optimization problem at every step, with the constraint that the solution has to lie in a probability simplex. We propose an efficient primal-dual based alternating direction method of multipliers algorithm and demonstrate its effectiveness for the problem of online portfolio selection with sector information. We show that our algorithm OLU-GS has sub-linear regret w.r.t. the best fixed and best shifting solution in hindsight. We successfully establish the robustness and scalability of OLU-GS by performing extensive experiments on two real-world datasets.
【Keywords】: Online learning; Portfolio selection; Group lasso; Non-smooth convex optimization; Alternating direction method of multipliers
【Paper Link】 【Pages】:1192-1198
【Authors】: Zhengming Ding ; Ming Shao ; Yun Fu
【Abstract】: We consider an interesting problem in this paper that uses transfer learning in two directions to compensate missing knowledge from the target domain. Transfer learning tends to be exploited as a powerful tool that mitigates the discrepancy between different databases used for knowledge transfer. It can also be used for knowledge transfer between different modalities within one database. However, in either case, transfer learning will fail if the target data are missing. To overcome this, we consider knowledge transfer between different databases and modalities simultaneously in a single framework, where missing target data from one database are recovered to facilitate recognition task. We referred to this framework as Latent Low-rank Transfer Subspace Learning method (L2TSL). We first propose to use a low-rank constraint as well as dictionary learning in a learned subspace to guide the knowledge transfer between and within different databases. We then introduce a latent factor to uncover the underlying structure of the missing target data. Next, transfer learning in two directions is proposed to integrate auxiliary database for transfer learning with missing target data. Experimental results of multi-modalities knowledge transfer with missing target data demonstrate that our method can successfully inherit knowledge from the auxiliary database to complete the target domain, and therefore enhance the performance when recognizing data from the modality without any training data.
【Keywords】: transfer learning; low-rank representation
【Paper Link】 【Pages】:1199-1205
【Authors】: Vincent Dumoulin ; Ian J. Goodfellow ; Aaron C. Courville ; Yoshua Bengio
【Abstract】: Restricted Boltzmann machines (RBMs) are powerful machine learning models, but learning and some kinds of inference in the model require sampling-based approximations, which, in classical digital computers, are implemented using expensive MCMC. Physical computation offers the opportunity to reduce the costof sampling by building physical systems whose natural dynamics correspond to drawing samples from the desired RBM distribution. Such a system avoids the burn-in and mixing cost of a Markov chain. However, hardware implementations of this variety usually entail limitations such as low-precision and limited range of the parameters and restrictions on the size and topology of the RBM. We conduct software simulations to determine how harmful each of these restrictions is. Our simulations are based on the D-Wave Two computer, but the issues we investigate arise in most forms of physical computation.Our findings suggest that designers of new physical computing hardware and algorithms for physical computers should focus their efforts on overcoming the limitations imposed by the topology restrictions of currently existing physical computers.
【Keywords】: RBM, restricted Boltzmann machine, physical computing
【Paper Link】 【Pages】:1206-1212
【Authors】: Xingyu Gao ; Steven C. H. Hoi ; Yongdong Zhang ; Ji Wan ; Jintao Li
【Abstract】: Image similarity search plays a key role in many multimediaapplications, where multimedia data (such as images and videos) areusually represented in high-dimensional feature space. In thispaper, we propose a novel Sparse Online Metric Learning (SOML)scheme for learning sparse distance functions from large-scalehigh-dimensional data and explore its application to imageretrieval. In contrast to many existing distance metric learningalgorithms that are often designed for low-dimensional data, theproposed algorithms are able to learn sparse distance metrics fromhigh-dimensional data in an efficient and scalable manner. Ourexperimental results show that the proposed method achieves betteror at least comparable accuracy performance than thestate-of-the-art non-sparse distance metric learning approaches, butenjoys a significant advantage in computational efficiency andsparsity, making it more practical for real-world applications.
【Keywords】:
【Paper Link】 【Pages】:1213-1220
【Authors】: Jonathan Grizou ; Iñaki Iturrate ; Luis Montesano ; Pierre-Yves Oudeyer ; Manuel Lopes
【Abstract】: Recent works have explored the use of brain signals to directly control virtual and robotic agents in sequential tasks. So far in such brain-computer interfaces (BCI), an explicit calibration phase was required to build a decoder that translates raw electroencephalography (EEG) signals from the brain of each user into meaningful instructions. This paper proposes a method that removes the calibration phase, and allows a user to control an agent to solve a sequential task. The proposed method assumes a distribution of possible tasks, and infers the interpretation of EEG signals and the task by selecting the hypothesis which best explains the history of interaction. We introduce a measure of uncertainty on the task and on the EEG signal interpretation to act as an exploratory bonus for a planning strategy. This speeds up learning by guiding the system to regions that better disambiguate among task hypotheses. We report experiments where four users use BCI to control an agent on a virtual world to reach a target without any previous calibration process.
【Keywords】: Brain-Computer Interaction; Interactive Learning; Adaptive interfaces; User Modeling; Planning under Uncertainty; Sequential Decision Making
【Paper Link】 【Pages】:1221-1227
【Authors】: Ghasem Heyrani-Nobari ; Tat-Seng Chua
【Abstract】: Online discussions are growing as a popular, effective and reliable source of information for users because of their liveliness, flexibility and up-to-date information. Online discussions are usually developed and advanced by groups of users with various backgrounds and intents. However because of their diversities in topics and issues discussed by the users, supervised methods are not able to accurately model such dynamic conditions. In this paper, we propose a novel unsupervised generative model to derive aspect-action pairs from online discussions. The proposed method simultaneously captures and models these two features with their relationships that exist in each thread. We assume that each user post is generated by a mixture of aspect and action topics. Therefore, we design a model that captures the latent factors that incorporates the aspect types and intended actions, which describe how users develop a topic in a discussion. In order to demonstrate the effectiveness of our approach, we empirically compare our model against the state of the art methods on large-scale discussion dataset, crawled from apple discussions with over 3.3 million user posts from 340k discussion threads.
【Keywords】: Online Discussions;Forums;Topic Model;Threads;Actions
【Paper Link】 【Pages】:1228-1234
【Authors】: Chengcheng Jia ; Guoqiang Zhong ; Yun Raymond Fu
【Abstract】: Tensor completion is an important topic in the area of image processing and computer vision research, which is generally built on extraction of the intrinsic structure of the tensor data. Drawing on this fact, action classification, relying heavily on the extracted features of high-dimensional tensors, may indeed benefit from tensor completion techniques. In this paper, we propose a low-rank tensor completion method for action classification, as well as image recovery. Since there may exist distortion and corruption in the tensor representations of video sequences, we project the tensors into a subspace, which contains the invariant structure of the tensors. In order to integrate useful supervisory information for classification, we adopt a discriminant analysis criterion to learn the projection matrices. The resulting multi-variate optimization problem can be effectively solved using the augmented Lagrange multiplier (ALM) algorithm. Experiments demonstrate that our method results with better accuracy compared with some other state-of-the-art low-rank tensor representation learning approaches on the MSR Hand Gesture 3D database and the MSR Action 3D database. By denoising the Multi-PIE face database, our experimental setup testifies the proposed method can also be employed to recover images.
【Keywords】:
【Paper Link】 【Pages】:1235-1241
【Authors】: Shenghua Liu ; Xueqi Cheng ; Fangtao Li
【Abstract】: Tweets ranking is important for information acquisition in Microblog. Due to the content sparsity and lackof labeled data, it is better to employ semi-supervisedlearning methods to utilize the unlabeled data. However,most of previous semi-supervised learning methods donot consider the pair conflict problem, which means thatthe new selected unlabeled data may conflict with the labeled and previously selected data. It will hurt the learning performance a lot, if the training data contains manyconflict pairs. In this paper, we propose a new collaborative semi-supervised SVM ranking model (CSR-TC)with consideration of the order conflict. The unlabeleddata is selected based on a dynamically maintained transitive closure graph to avoid pair conflict. We also investigate the two views of features, intrinsic and contentrelevant features, for the proposed model. Extensive experiments are conducted on TREC Microblogging corpus. The results demonstrate that our proposed methodachieves significant improvement, compared to severalstate-of-the-art models.
【Keywords】: Microblog search, ranking tweets, co-training, semi-supervised learning, transitive closure
【Paper Link】 【Pages】:1242-1250
【Authors】: James Robert Lloyd ; David K. Duvenaud ; Roger B. Grosse ; Joshua B. Tenenbaum ; Zoubin Ghahramani
【Abstract】: This paper presents the beginnings of an automatic statistician, focusing on regression problems. Our system explores an open-ended space of statistical models to discover a good explanation of a data set, and then produces a detailed report with figures and natural-language text. Our approach treats unknown regression functions nonparametrically using Gaussian processes, which has two important consequences. First, Gaussian processes can model functions in terms of high-level properties (e.g. smoothness, trends, periodicity, changepoints). Taken together with the compositional structure of our language of models this allows us to automatically describe functions in simple terms. Second, the use of flexible nonparametric models and a rich language for composing them in an open-ended manner also results in state-of-the-art extrapolation performance evaluated over 13 real time series data sets from various domains.
【Keywords】: Automatic statistician; Gaussian process; Time series; Natural language; Nonparametric; Regression
【Paper Link】 【Pages】:1251-1257
【Authors】: Canyi Lu ; Yunchao Wei ; Zhouchen Lin ; Shuicheng Yan
【Abstract】: This paper proposes the Proximal Iteratively REweighted (PIRE) algorithm for solving a general problem, which involves a large body of nonconvex sparse and structured sparse related problems. Comparing with previous iterative solvers for nonconvex sparse problem, PIRE is much more general and efficient. The computational cost of PIRE in each iteration is usually as low as the state-of-the-art convex solvers. We further propose the PIRE algorithm with Parallel Splitting (PIRE-PS) and PIRE algorithm with Alternative Updating (PIRE-AU) to handle the multi-variable problems. In theory, we prove that our proposed methods converge and any limit solution is a stationary point. Extensive experiments on both synthesis and real data sets demonstrate that our methods achieve comparative learning performance, but are much more efficient, by comparing with previous nonconvex solvers.
【Keywords】: nonconvex sparse optimzation
【Paper Link】 【Pages】:1258-1264
【Authors】: Zhiwu Lu ; Liwei Wang ; Ji-Rong Wen
【Abstract】: This paper presents a direct semantic analysis method for learning the correlation matrix between visual and textual words from socially tagged images. In the literature, to improve the traditional visual bag-of-words (BOW) representation, latent semantic analysis has been studied extensively for learning a compact visual representation, where each visual word may be related to multiple latent topics. However, these latent topics do not convey any true semantic information which can be understood by human. In fact, it remains a challenging problem how to recover the relationships between visual and textual words. Motivated by the recent advances in dealing with socially tagged images, we develop a direct semantic analysis method which can explicitly learn the correlation matrix between visual and textual words for social image classification. To this end, we formulate our direct semantic analysis from a graph-based learning viewpoint. Once the correlation matrix is learnt, we can readily first obtain a semantically refined visual BOW representation and then apply it to social image classification. Experimental results on two benchmark image datasets show the promising performance of the proposed method.
【Keywords】:
【Paper Link】 【Pages】:1265-1271
【Authors】: Kelly Moran ; Byron C. Wallace ; Carla E. Brodley
【Abstract】: Selecting good conference keywords is important because they often determine the composition of review committees and hence which papers are reviewed by whom. But presently conference keywords are generated in an ad-hoc manner by a small set of conference organizers. This approach is plainly not ideal. There is no guarantee, for example, that the generated keyword set aligns with what the community is actually working on and submitting to the conference in a given year. This is especially true in fast moving fields such as AI. The problem is exacerbated by the tendency of organizers to draw heavily on preceding years' keyword lists when generating a new set. Rather than a select few ordaining a keyword set that that represents AI at large, it would be preferable to generate these keywords more directly from the data, with input from research community members. To this end, we solicited feedback from seven AAAI PC members regarding a previously existing keyword set and used these 'community-sourced constraints' to inform a clustering over the abstracts of all submissions to AAAI 2013. We show that the keywords discovered via this data-driven, human-in-the-loop method are at least as preferred (by AAAI PC members) as 2013's manually generated set, and that they include categories previously overlooked by organizers. Many of the discovered terms were used for this year's conference.
【Keywords】: clustering; crowdsourcing; concept drift; text mining; constraints
【Paper Link】 【Pages】:1272-1278
【Authors】: Arti Ramesh ; Dan Goldwasser ; Bert Huang ; Hal Daumé III ; Lise Getoor
【Abstract】: Maintaining and cultivating student engagement is critical for learning. Understanding factors affecting student engagement will help in designing better courses and improving student retention. The large number of participants in massive open online courses (MOOCs) and data collected from their interaction with the MOOC open up avenues for studying student engagement at scale. In this work, we develop a framework for modeling and understanding student engagement in online courses based on student behavioral cues. Our first contribution is the abstraction of student engagement types using latent representations and using that in a probabilistic model to connect student behavior with course completion. We demonstrate that the latent formulation for engagement helps in predicting student survival across three MOOCs. Next, in order to initiate better instructor interventions, we need to be able to predict student survival early in the course. We demonstrate that we can predict student survival early in the course reliably using the latent model. Finally, we perform a closer quantitative analysis of user interaction with the MOOC and identify student activities that are good indicators for survival at different points in the course.
【Keywords】: MOOCs, student engagement, predictive modeling, statistical relational learning, student survival
【Paper Link】 【Pages】:1279-1285
【Authors】: Fanhua Shang ; Yuanyuan Liu ; James Cheng
【Abstract】: Higher-order tensors are becoming prevalent in many scientific areas such as computer vision, social network analysis, data mining and neuroscience. Traditional tensor decomposition approaches face three major challenges: model selecting, gross corruptions and computational efficiency. To address these problems, we first propose a parallel trace norm regularized tensor decomposition method, and formulate it as a convex optimization problem. This mehtod does not require the rank of each mode to be specified beforehand, and can automaticaly determine the number of factors in each mode through our optimization scheme. By considering the low-rank structure of the observed tensor, we analyze the equivalent relationship of the trace norm between a low-rank tensor and its core tensor. Then, we cast a non-convex tensor decomposition model into a weighted combination of multiple much smaller-scale matrix trace norm minimization. Finally, we develop two parallel alternating direction methods of multipliers (ADMM) to solve our problems. Experimental results verify that our regularized formulation is effective, and our methods are robust to noise or outliers.
【Keywords】:
【Paper Link】 【Pages】:1286-1292
【Authors】: Weiwei Shen ; Jun Wang ; Shiqian Ma
【Abstract】: Due to recent empirical success, machine learning algorithms have drawn sufficient attention and are becoming important analysis tools in financial industry. In particular, as the core engine of many financial services such as private wealth and pension fund management, portfolio management calls for the application of those novel algorithms. Most of portfolio allocation strategies do not account for costs from market frictions such as transaction costs and capital gain taxes, as the complexity of sensible cost models often causes the induced problem intractable. In this paper, we propose a doubly regularized portfolio that provides a modest but effective solution to the above difficulty. Specifically, as all kinds of trading costs primarily root in large transaction volumes, to reduce volumes we synergistically combine two penalty terms with classic risk minimization models to ensure: (1) only a small set of assets are selected to invest in each period; (2) portfolios in consecutive trading periods are similar. To assess the new portfolio, we apply standard evaluation criteria and conduct extensive experiments on well-known benchmarks and market datasets. Compared with various state-of-the-art portfolios, the proposed portfolio demonstrates a superior performance of having both higher risk-adjusted returns and dramatically decreased transaction volumes.
【Keywords】: Portfolio Optimization, Risk Minimization, Structure Regularization
【Paper Link】 【Pages】:1293-1299
【Authors】: Fei Tian ; Bin Gao ; Qing Cui ; Enhong Chen ; Tie-Yan Liu
【Abstract】: Recently deep learning has been successfully adopted in many applications such as speech recognition and image classification. In this work, we explore the possibility of employing deep learning in graph clustering. We propose a simple method, which first learns a nonlinear embedding of the original graph by stacked autoencoder, and then runs $k$-means algorithm on the embedding to obtain the clustering result. We show that this simple method has solid theoretical foundation, due to the similarity between autoencoder and spectral clustering in terms of what they actually optimize. Then, we demonstrate that the proposed method is more efficient and flexible than spectral clustering. First, the computational complexity of autoencoder is much lower than spectral clustering: the former can be linear to the number of nodes in a sparse graph while the latter is super quadratic due to eigenvalue decomposition. Second, when additional sparsity constraint is imposed, we can simply employ the sparse autoencoder developed in the literature of deep learning; however, it is non-straightforward to implement a sparse spectral method. The experimental results on various graph datasets show that the proposed method significantly outperforms conventional spectral clustering which clearly indicates the effectiveness of deep learning in graph clustering.
【Keywords】: deep representations;clustering on graph;neural networks
【Paper Link】 【Pages】:1300-1306
【Authors】: Fei Tian ; Haifang Li ; Wei Chen ; Tao Qin ; Enhong Chen ; Tie-Yan Liu
【Abstract】: Machine learning algorithms have been applied to predict agent behaviors in real-world dynamic systems, such as advertiser behaviors in sponsored search and worker behaviors in crowdsourcing. Behavior data in these systems are generated by live agents: once systems change due to adoption of prediction models learnt from behavior data, agents will observe and respond to these changes by changing their own behaviors accordingly. Therefore, the evolving behavior data will not be identically and independently distributed, posing great challenges to theoretical analysis. To tackle this challenge, in this paper, we propose to use Markov Chain in Random Environments (MCRE) to describe the behavior data, and perform generalization analysis of machine learning algorithms on its basis. We propose a novel technique that transforms the original time-variant MCRE into a higher-dimensional time-homogeneous Markov chain, which is easier to deal with. We prove the convergence of the new Markov chain when time approaches infinity. Then we obtain a generalization bound for the machine learning algorithms on the behavior data generated by the new Markov chain. To the best of our knowledge, this is the first work that performs the generalization analysis on data generated by complex processes in real-world dynamic systems.
【Keywords】: generalization analysis;agent behavior model prediction;Markov Chain in Random Environments;empirical risk minimization algorithm
【Paper Link】 【Pages】:1307-1313
【Authors】: Alexander Van Esbroeck ; Satinder P. Singh ; Ilan Rubinfeld ; Zeeshan Syed
【Abstract】: Missing values are a common problem when applying classification algorithms to real-world medical data. This is especially true for trauma patients, where the emergent nature of the cases makes it difficult to collect all of the relevant data for each patient. Standard methods for handling missingness first learn a model to estimate missing data values, and subsequently train and evaluate a classifier using data imputed with this model. Recently, several proposed methods have demonstrated the benefits of jointly estimating the imputation model and classifier parameters. However, these methods make assumptions that limit their utility with many real-world medical datasets. For example, the assumption that data elements are missing at random is often invalid. We address this situation by exploring a novel approach for jointly learning the imputation model and classifier. Unlike previous algorithms, our approach makes no assumptions about the missingness of the data, can be used with arbitrary probabilistic data models and classification loss functions, and can be used when both the training and testing data have missing values. We investigate the utility of this approach on the prediction of several patient outcomes in a large national registry of trauma patients, and find that it significantly outperforms standard sequential methods.
【Keywords】: missing values; trauma
【Paper Link】 【Pages】:1314-1320
【Authors】: Byron C. Wallace ; Issa J. Dahabreh ; Thomas A. Trikalinos ; Michael Barton Laws ; Ira B. Wilson ; Eugene Charniak
【Abstract】: We consider the task of grouping doctors with respect to communication patterns exhibited in outpatient visits. We propose a novel approach toward this end in which we model speech act transitions in conversations via a log-linear model incorporating physician specific components. We train this model over transcripts of outpatient visits annotated with speech act codes and then cluster physicians in (a transformation of) this parameter space. We find significant correlations between the induced groupings and patient survey response data comprising ratings of physician communication. Furthermore, the novel sequential component model we leverage to induce this clustering allows us to explore differences across these groups. This work demonstrates how statistical AI might be used to better understand (and ultimately improve) physician communication.
【Keywords】: patient-doctor communication; speech acts; machine learning; natural language processing; dialogue
【Paper Link】 【Pages】:1321-1327
【Authors】: Dong Wang ; Xiaoyang Tan
【Abstract】: Many distance learning algorithms have been developed in recent years. However, few of them consider the problem when the class labels of training data are noisy, and this may lead to serious performance deterioration. In this paper, we present a robust distance learning method in the presence of label noise, by extending a previous non-parametric discriminative distance learning algorithm, i.e., Neighbourhood Components Analysis (NCA). Particularly, we analyze the effect of label noise on the derivative of likelihood with respect to the transformation matrix, and propose to model the conditional probability of the true label of each point so as to reduce that effect. The model is then optimized within the EM framework, with additional regularization used to avoid overfitting. Our experiments on several UCI datasets and a real dataset with unknown noise patterns show that the proposed RNCA is more tolerant to class label noise compared to the original NCA method.
【Keywords】:
【Paper Link】 【Pages】:1328-1333
【Authors】: Hua Wang ; Feiping Nie ; Heng Huang
【Abstract】: In this paper, we propose an unsupervised projection method for feature extraction to preserve both global and local consistencies of the input data in the projected space. Traditional unsupervised feature extraction methods, such as principal component analysis (PCA) and locality preserving projections (LPP), can only explore either the global or local geometric structures of the input data, but not the both at the same time. In our new method, we introduce a new measurement using the neighborhood data variances to assess the data locality, by which we propose to learn an optimal projection by rewarding both the global and local structures of the input data. The formulated optimization problem is challenging to solve, because it ends up a trace ratio minimization problem. In this paper, as an important theoretical contribution, we propose a simple yet efficient optimization algorithm to solve the trace ratio problem with theoretically proved convergence. Extensive experiments have been performed on six benchmark data sets, where the promising results validate the proposed method.
【Keywords】: Unsupervised Dimensionality Reduction
【Paper Link】 【Pages】:1334-1340
【Authors】: Qifan Wang ; Lingyun Ruan ; Luo Si
【Abstract】: Multiple Instance Learning (MIL) is a popular learning technique in various vision tasks including image classification. However, most existing MIL methods do not consider the problem of insufficient examples in the given target category. In this case, it is difficult for traditional MIL methods to build an accurate classifier due to the lack of training examples. Motivated by the empirical success of transfer learning, this paper proposes a novel approach of Adaptive Knowledge Transfer for Multiple Instance Learning (AKT-MIL) in image classification. The new method transfers cross-category knowledge from source categories under multiple instance setting for boosting the learning process. A unified learning framework with a data-dependent mixture model is designed to adaptively combine the transferred knowledge from sources with a weak classifier built in the target domain. Based on this framework, an iterative coordinate descent method with Constraint Concave-Convex Programming (CCCP) is proposed as the optimization procedure. An extensive set of experimental results demonstrate that the proposed AKT-MIL approach substantially outperforms several state-of-the-art algorithms on two benchmark datasets, especially in the scenario when very few training examples are available in the target domain.
【Keywords】: transfer learning; multiple instance learning; image classification
【Paper Link】 【Pages】:1341-1347
【Authors】: Jinfeng Yi ; Jun Wang ; Rong Jin
【Abstract】: Sensitive data such as medical records and business reports usually contains valuable information that can be used to build prediction models. However, designing learning models by directly using sensitive data might result in severe privacy and copyright issues. In this paper, we propose a novel matrix completion based framework that aims to tackle two challenging issues simultaneously: i) handling missing and noisy sensitive data, and ii) preserving the privacy of the sensitive data during the learning process. In particular, the proposed framework is able to mask the sensitive data while ensuring that the transformed data are still usable for training regression models. We show that two key properties, namely model preserving and privacy preserving, are satisfied by the transformed data obtained from the proposed framework. In model preserving, we guarantee that the linear regression model built from the masked data approximates the regression model learned from the original data in a perfect way. In privacy preserving, we ensure that the original sensitive data cannot be recovered since the transformation procedure is irreversible. Given these two characteristics, the transformed data can be safely released to any learners for designing prediction models without revealing any private content. Our empirical studies with a synthesized dataset and multiple sensitive benchmark datasets verify our theoretical claim as well as the effectiveness of the proposed framework.
【Keywords】: Regression, Privacy, Matrix Completion
【Paper Link】 【Pages】:1348-1354
【Authors】: Onur Yürüten ; Jiyong Zhang ; Pearl Pu
【Abstract】: The modern sensor technology helps us collect time series data for activities of daily living (ADLs), which in turn can be used to infer broad patterns, such as common daily routines. Most of the existing approaches either rely on a model trained by a preselected and manually labeled set of activities, or perform micro-pattern analysis with manually selected length and number of micro-patterns. Since real life ADL datasets are massive, such approaches would be too costly to apply. Thus, there is a need to formulate unsupervised methods that can be applied to different time scales.We propose a novel approach to discover clusters of daily activity routines.We use a matrix decomposition method to isolate routines and deviations to obtain two different sets of clusters. We obtain the final memberships via the cross product of these sets. We validate our approach using two real-life ADL datasets and a well-known artificial dataset. Based on average silhouette width scores, our approach can capture strong structures in the underlying data. Furthermore, results show that our approach improves on the accuracy of the baseline algorithms by 12% with a statistical significance (p < 0.05) using the Wilcoxon signed-rank comparison test.
【Keywords】: activity pattern analysis; routine clustering
【Paper Link】 【Pages】:1355-1361
【Authors】: Miao Zhang ; Chris H. Q. Ding ; Ya Zhang ; Feiping Nie
【Abstract】: Feature selection plays an important role in many machine learning and data mining applications. In this paper, we propose to use L2,p norm for feature selection with emphasis on small p. As p approaches 0, feature selection becomes discrete feature selection problem. We provide two algorithms, proximal gradient algorithm and rank one update algorithm, which is more efficient at large regularization. We provide closed form solutions of the proximal operator at p = 0, 1/2. Experiments onreal life datasets show that features selected at small p consistently outperform features selected at p = 1, the standard L2,1 approach and other popular feature selection methods.
【Keywords】: Feature selection; sparse limit
【Paper Link】 【Pages】:1362-1368
【Authors】: Xiaoqin Zhang ; Zhengyuan Zhou ; Di Wang ; Yi Ma
【Abstract】: In this paper, we study the low-rank tensor completion problem, where a high-order tensor with missing entries is given and the goal is to complete the tensor. We propose to minimize a new convex objective function, based on log sum of exponentials of nuclear norms, that promotes the low-rankness of unfolding matrices of the completed tensor. We show for the first time that the proximal operator to this objective function is readily computable through a hybrid singular value thresholding scheme. This leads to a new solution to high-order (low-rank) tensor completion via convex relaxation. We show that this convex relaxation and the resulting solution are much more effective than existing tensor completion methods (including those also based on minimizing ranks of unfolding matrices). The hybrid singular value thresholding scheme can be applied to any problem where the goal is to minimize the maximum rank of a set of low-rank matrices.
【Keywords】: Singular Value Thresholding; Tensor Completion
【Paper Link】 【Pages】:1369-1375
【Authors】: Yuyu Zhang ; Hanjun Dai ; Chang Xu ; Jun Feng ; Taifeng Wang ; Jiang Bian ; Bin Wang ; Tie-Yan Liu
【Abstract】: Click prediction is one of the fundamental problems in sponsored search. Most of existing studies took advantage of machine learning approaches to predict ad click for each event of ad view independently. However, as observed in the real-world sponsored search system, user's behaviors on ads yield high dependency on how the user behaved along with the past time, especially in terms of what queries she submitted, what ads she clicked or ignored, and how long she spent on the landing pages of clicked ads, etc. Inspired by these observations, we introduce a novel framework based on Recurrent Neural Networks (RNN). Compared to traditional methods, this framework directly models the dependency on user's sequential behaviors into the click prediction process through the recurrent structure in RNN. Large scale evaluations on the click-through logs from a commercial search engine demonstrate that our approach can significantly improve the click prediction accuracy, compared to sequence-independent approaches.
【Keywords】:
【Paper Link】 【Pages】:1376-1383
【Authors】: Shuai Zheng ; James T. Kwok
【Abstract】: Accurately measuring the aerosol optical depth (AOD) is essential for our understanding of the climate. Currently, AOD can be measured by (i) satellite instruments, which operate on a global scale but have limited accuracies; and (ii) ground-based instruments, which are more accurate but not widely available. Recent approaches focus on integrating measurements from these two sources to complement each other. In this paper, we further improve the prediction accuracy by using the observation that the AOD varies slowly in the spatial domain. Using a probabilistic approach, we impose this smoothness constraint by a Gaussian random field on the Earth's surface, which can be considered as a two-dimensional manifold. The proposed integration approach is computationally simple, and experimental results on both synthetic and real-world data sets show that it significantly outperforms the state-of-the-art.
【Keywords】: aerosol optical depth; manifold; Gaussian random field
【Paper Link】 【Pages】:1384-1390
【Authors】: Sofia Amador ; Steven Okamoto ; Roie Zivan
【Abstract】: Realistic multi-agent team applications often feature dynamic environments with soft deadlines that penalize late execution of tasks. This puts a premium on quickly allocating tasks to agents, but finding the optimal allocation is NP-hard due to temporal and spatial constraints that require tasks to be executed sequentially by agents. We propose FMC_TA, a novel task allocation algorithm that allows tasks to be easily sequenced to yield high-quality solutions. FMC_TA first finds allocations that are fair (envy-free), balancing the load and sharing important tasks between agents, and efficient (Pareto optimal) in a simplified version of the problem. It computes such allocations in polynomial or pseudo-polynomial time (centrally or distributedly, respectively) using a Fisher market with agents as buyers and tasks as goods. It then heuristically schedules the allocations, taking into account inter-agent constraints on shared tasks. We empirically compare our algorithm to state-of-the-art incomplete methods, both centralized and distributed, on law enforcement problems inspired by real police logs. The results show a clear advantage for FMC_TA both in total utility and in other measures commonly used by law enforcement authorities.
【Keywords】: Task Allocation; Market Equilibrium
【Paper Link】 【Pages】:1391-1397
【Authors】: Craig Boutilier ; Jérôme Lang ; Joel Oren ; Héctor Palacios
【Abstract】: We consider voting situations in which some candidates may turn out to be unavailable. When determining availability is costly (e.g., in terms of money, time, or computation), voting prior to determining candidate availability and testing the winner's availability after the vote may be beneficial. However, since few voting rules are robust to candidate deletion, winner determination requires a number of such availability tests. We outline a model for analyzing such problems, defining robust winners relative to potential candidate unavailability. We assess the complexity of computing robust winners for several voting rules. Assuming a distribution over availability, and costs for availability tests/queries, we describe algorithms for computing optimal query policies, which minimize the expected cost of determining true winners.
【Keywords】: social choice; voting; possible winners; robust winners; query policies; multi agent systems
【Paper Link】 【Pages】:1398-1404
【Authors】: Robert Bredereck ; Jiehua Chen ; Piotr Faliszewski ; André Nichterlein ; Rolf Niedermeier
【Abstract】: In the Shift Bribery problem, we are given an election (based on preference orders), a preferred candidate p, and a budget. The goal is to ensure that p wins by shifting p higher in some voters' preference orders. However, each such shift request comes at a price (depending on the voter and on the extent of the shift) and we must not exceed the given budget. We study the parameterized computational complexity of Shift Bribery with respect to a number of parameters (pertaining to the nature of the solution sought and the size of the election) and several classes of price functions. When we parameterize Shift Bribery by the number of affected voters, then for each of our voting rules (Borda, Maximin, Copeland) the problem is W[2]-hard. If, instead, we parameterize by the number of positions by which p is shifted in total, then the problem is fixed-parameter tractable for Borda and Maximin, and is W[1]-hard for Copeland. If we parameterize by the budget for the cost of shifting, then the results depend on the price function class. We also show that Shift Bribery tends to be tractable when parameterized by the number of voters, but that the results for the number of candidates are more enigmatic.
【Keywords】: Preferenced-based voting; Campaign management; Computational (in)tractability; Parameterized complexity analysis; Approximation
【Paper Link】 【Pages】:1405-1411
【Authors】: John P. Dickerson ; Jonathan R. Goldman ; Jeremy Karp ; Ariel D. Procaccia ; Tuomas Sandholm
【Abstract】: The fair division of indivisible goods has long been an important topic in economics and, more recently, computer science. We investigate the existence of envy-free allocations of indivisible goods, that is, allocations where each player values her own allocated set of goods at least as highly as any other player's allocated set of goods. Under additive valuations, we show that even when the number of goods is larger than the number of agents by a linear fraction, envy-free allocations are unlikely to exist. We then show that when the number of goods is larger by a logarithmic factor, such allocations exist with high probability. We support these results experimentally and show that the asymptotic behavior of the theory holds even when the number of goods and agents is quite small. We demonstrate that there is a sharp phase transition from nonexistence to existence of envy-free allocations, and that on average the computational problem is hardest at that transition.
【Keywords】: Fair division; Social choice; Envy-free allocation; Phase transition
【Paper Link】 【Pages】:1412-1418
【Authors】: John P. Dickerson ; Tuomas Sandholm
【Abstract】: Kidney exchange, where candidates with organ failure trade incompatible but willing donors, is a life-saving alternative to the deceased donor waitlist, which has inadequate supply to meet demand. While fielded kidney exchanges see huge benefit from altruistic kidney donors (who give an organ without a paired needy candidate), a significantly higher medical risk to the donor deters similar altruism with livers. In this paper, we begin by proposing the idea of liver exchange, and show on demographically accurate data that vetted kidney exchange algorithms can be adapted to clear such an exchange at the nationwide level. We then explore cross-organ donation where kidneys and livers can be bartered for each other. We show theoretically that this multi-organ exchange provides linearly more transplants than running separate kidney and liver exchanges; this linear gain is a product of altruistic kidney donors creating chains that thread through the liver pool. We support this result experimentally on demographically accurate multi-organ exchanges. We conclude with thoughts regarding the fielding of a nationwide liver or joint liver-kidney exchange from a legal and computational point of view.
【Keywords】: Kidney exchange; Liver exchange; Multi-organ exchange; Cycle cover
【Paper Link】 【Pages】:1419-1425
【Authors】: Ning Ding ; Fangzhen Lin
【Abstract】: Open list proportional representation is an election mechanism used in many elections, including the 2012 Hong Kong Legislative Council Geographical Constituencies election. In this paper, we assume that there are just two parties in the election, and that the number of votes that a list would get is the sum of the numbers of votes that the candidates in the list would get if each of them would go alone in the election. Under these assumptions, we formulate the election as a mostly zero-sum game, and show that while the game always has a pure Nash equilibrium, it is NP-hard to compute it.
【Keywords】: Open List Proportional Representation; Hong Kong Legislative Coucil election
【Paper Link】 【Pages】:1426-1432
【Authors】: Xiaowei Huang ; Ron van der Meyden
【Abstract】: This paper presents a symbolic BDD-based model checking algorithm for an epistemic strategy logic with observational semantics. The logic has been shown to be more expressive than several variants of ATELand therefore the algorithm can also be used for ATEL model checking. We implement the algorithm in a model checker and apply it to an application on train control system. The performance of the algorithm is also reported, with a comparison showing improved results over a previous partially symbolic approach for ATEL model checking.
【Keywords】: Model checking; strategy; the logic of knowledge
【Paper Link】 【Pages】:1433-1439
【Authors】: Yicheng Liu ; Pingzhong Tang ; Wenyi Fang
【Abstract】: Stability is a central concept in exchange-based mechanismdesign. It imposes a fundamental requirement that no subsetof agents could beneficially deviate from the outcome pre-scribed by the mechanism. However, deployment of stabilityin an exchange mechanism presents at least two challenges.First, it reduces social welfare and sometimes prevents themechanism from producing a solution. Second, it might incurcomputational cost to clear the mechanism.In this paper, we propose an alternative notion of stability,coined internal stability, under which we analyze the socialwelfare bounds and computational complexity. Our contribu-tions are as follows: for both pairwise matchings and limited-length exchanges, for both unweighted and weighted graph-s, (1) we prove desirable tight social welfare bounds; (2) weanalyze the computational complexity for clearing the match-ings and exchanges. Extensive experiments on the kidney ex-change domain demonstrate that the optimal welfare underinternal stability is very close to the unconstrained optimal.
【Keywords】: internally stable; kidney exchange; welfare loss
【Paper Link】 【Pages】:1440-1446
【Authors】: Benny Lutati ; Vadim Levit ; Tal Grinshpoun ; Amnon Meisels
【Abstract】: A model of the problem of charging and discharging electrical vehicles as a congestion game is presented. A generalization of congestion games - feedback congestion games (FCG) - is introduced. The charging of grid-integrated vehicles, which can also discharge energy back to the grid, is a natural FCG application. FCGs are proven to be exact potential games and therefore converge to a pure-strategy Nash equilibrium by an iterated better-response process. A compact representation and an algorithm that enable efficient best-response search are presented. A detailed empirical evaluation assesses the performance of the iterated best-response process. The evaluation considers the quality of the resulting solutions and the rate of convergence to a stable state. The effect of allowing to also discharge batteries using FCG is compared to scenarios that only include charging and is found to dramatically improve the predictability of the achieved solutions as well as the balancing of load.
【Keywords】: Congestion games; Potential games; EV charging; V2G; GIV; Iterated best response
【Paper Link】 【Pages】:1447-1455
【Authors】: Duc Thien Nguyen ; William Yeoh ; Hoong Chuin Lau ; Shlomo Zilberstein ; Chongjie Zhang
【Abstract】: Researchers have introduced the Dynamic Distributed Constraint Optimization Problem (Dynamic DCOP) formulation to model dynamically changing multi-agent coordination problems, where a dynamic DCOP is a sequence of (static canonical) DCOPs, each partially different from the DCOP preceding it. Existing work typically assumes that the problem in each time step is decoupled from the problems in other time steps, which might not hold in some applications. Therefore, in this paper, we make the following contributions: (i) We introduce a new model, called Markovian Dynamic DCOPs (MD-DCOPs), where the DCOP in the next time step is a function of the value assignments in the current time step; (ii) We introduce two distributed reinforcement learning algorithms, the Distributed RVI Q-learning algorithm and the Distributed R-learning algorithm, that balance exploration and exploitation to solve MD-DCOPs in an online manner; and (iii) We empirically evaluate them against an existing multi-arm bandit DCOP algorithm on dynamic DCOPs.
【Keywords】:
【Paper Link】 【Pages】:1456-1462
【Authors】: Joel Oren ; Brendan Lucier
【Abstract】: We consider a classic social choice problem in an online setting. In each round, a decision maker observes a single agent's preferences overa set of $m$ candidates, and must choose whether to irrevocably add a candidate to a selection set of limited cardinality $k$. Each agent's (positional) score depends on the candidates in the set when he arrives, and the decision-maker's goal is to maximize average (over all agents) score. We prove that no algorithm (even randomized) can achieve an approximationfactor better than $O(\frac{\log\log m}{\log m})$. In contrast, if the agents arrive in random order, we present a $(1 - \frac{1}{e} - o(1))$-approximatealgorithm, matching a lower bound for the off-line problem.We show that improved performance is possible for natural input distributionsor scoring rules. Finally, if the algorithm is permitted to revoke decisions at a fixedcost, we apply regret-minimization techniques to achieve approximation $1 - \frac{1}{e} - o(1)$ even for arbitrary inputs.
【Keywords】: online algorithms;competitive analysis;multi-winner social choice
【Paper Link】 【Pages】:1463-1470
【Authors】: Joel Oren ; Nina Narodytska ; Craig Boutilier
【Abstract】: Vendors of all types face the problem of selecting a slate of product offerings—their assortment or catalog—that will maximize their profits. The profitability of a catalog is determined by both customer preferences and the offerings of their competitors. We develop a game-theoretic model for analyzing the vendor catalog optimization problem in the face of competing vendors. We show that computing a best response is intractable in general, but can be solved by dynamic programming given certain informational or structural assumptions about consumer preferences. We also analyze conditions under which pure Nash equilibria exist and provide several price of anarchy/stability results
【Keywords】: price of anarchy;price of stability;best response
【Paper Link】 【Pages】:1471-1477
【Authors】: Bijan Ranjbar Sahraei ; Haitham Bou-Ammar ; Daan Bloembergen ; Karl Tuyls ; Gerhard Weiss
【Abstract】: This paper presents a theoretical as well as empirical study on the evolution of cooperation on complex social networks, following the continuous action iterated prisoner's dilemma (CAIPD) model. In particular, convergence to network-wide agreement is proven for both evolutionary networks with fixed interaction dynamics, as well as for coevolutionary networks where these dynamics change over time. Moreover, an extension to the CAIPD model is proposed that allows to model influence on the evolution of cooperation in social networks. As such, this work contributes to a better understanding of behavioral change on social networks, and provides a first step towards their active control.
【Keywords】: Evolution of Cooperation; Graph Laplacian; Agreement Dynamics
【Paper Link】 【Pages】:1478-1484
【Authors】: Jason Sleight ; Edmund H. Durfee
【Abstract】: We formulate an approach to multiagent metareasoning that uses organizational design to focus each agent's reasoning on the aspects of its local problem that let it make the most worthwhile contributions to joint behavior. By employing the decentralized Markov decision process framework, we characterize an organizational design problem that explicitly considers the quantitative impact that a design has on both the quality of the agents' behaviors and their reasoning costs. We describe an automated organizational design process that can approximately solve our organizational design problem via incremental search, and present techniques that efficiently estimate the incremental impact of a candidate organizational influence. Our empirical evaluation confirms that our process generates organizational designs that impart a desired metareasoning regime upon the agents.
【Keywords】: organizational design; multiagent metareasoning; Dec-MDP
【Paper Link】 【Pages】:1485-1491
【Authors】: Leandro Soriano Marcolino ; Haifeng Xu ; Albert Xin Jiang ; Milind Tambe ; Emma Bowring
【Abstract】: Recent work has shown that diverse teams can outperform a uniform team made of copies of the best agent. However, there are fundamental questions that were not asked before. When should we use diverse or uniform teams? How does the performance change as the action space or the teams get larger? Hence, we present a new model of diversity for teams, that is more general than previous models. We prove that the performance of a diverse team improves as the size of the action space gets larger. Concerning the size of the diverse team, we show that the performance converges exponentially fast to the optimal one as we increase the number of agents. We present synthetic experiments that allow us to gain further insights: even though a diverse team outperforms a uniform team when the size of the action space increases, the uniform team will eventually again play better than the diverse team for a large enough action space. We verify our predictions in a system of Go playing agents, where we show a diverse team that improves in performance as the board size increases, and eventually overcomes a uniform team.
【Keywords】: Team formation; Coordination & Collaboration; Distributed AI
【Paper Link】 【Pages】:1492-1499
【Authors】: Feng Wu ; Nicholas R. Jennings
【Abstract】: Many multi-agent coordination problems can be represented as DCOPs. Motivated by task allocation in disaster response, we extend standard DCOP models to consider uncertain task rewards where the outcome of completing a task depends on its current state, which is randomly drawn from unknown distributions. The goal of solving this problem is to find a solution for all agents that minimizes the overall worst-case loss. This is a challenging problem for centralized algorithms because the search space grows exponentially with the number of agents and is nontrivial for existing algorithms for standard DCOPs. To address this, we propose a novel decentralized algorithm that incorporates Max-Sum with iterative constraint generation to solve the problem by passing messages among agents. By so doing, our approach scales well and can solve instances of the task allocation problem with hundreds of agents and tasks.
【Keywords】: Multi-Agent Systems; DCOP
【Paper Link】 【Pages】:1500-1506
【Authors】: Haifeng Xu ; Fei Fang ; Albert Xin Jiang ; Vincent Conitzer ; Shaddin Dughmi ; Milind Tambe
【Abstract】: Among the many deployment areas of Stackelberg Security games, a major area involves games played out in space and time, which includes applications in multiple mobile defender resources protecting multiple mobile targets. Previous algorithms for such spatio-temporal security games fail to scale-up and little is known ofthe computational complexity properties of these problems.This paper provides a novel oracle-based algorithmic framework for a systematic study of different problem variants of computing optimal (minimax) strategies in spatio-temporal security games. Our framework enables efficient computation of a minimax strategy when the problem admits a polynomial-time oracle. Furthermore,for the cases in which efficient oracles are difficultto find, we propose approximations or prove hardness results.
【Keywords】: Security Games; Zero-Sum Games; Minimax Equilibrium; Oracle; Equilibria Computation
【Paper Link】 【Pages】:1507-1514
【Authors】: Xiaoqin Shelley Zhang ; Mark Klein ; Ivan Marsá-Maestre
【Abstract】: A large number of interdependent issues in complex contract negotiation poses a significant challenge for current approaches, which becomes even more apparent when negotiation problems scale up. To address this challenge, we present a structured anytime search process with an agenda management mechanism using a hierarchical negotiation model, where agents search at various levels during the negotiation with the guidance of a mediator. This structured negotiation process increases computational efficiency, making negotiations scalable for large number of interdependent issues. To validate the contributions of our approach, 1) we developed our proposed negotiation model using a hierarchical problem structure and a constraint-based preference model for real-world applications; 2) we defined a scenario matrix to capture various characteristics of negotiation scenarios and developed a scenario generator that produces test cases according to this matrix; and 3) we performed an extensive set of experiments to study the performance of this structured negotiation protocol and the influence of different scenario parameters, and investigated the Pareto efficiency and social welfare optimality of the negotiation outcomes. The experimental result supports the hypothesis that this hierarchical negotiation approach greatly improves scalability with the complexity of the negotiation scenarios.
【Keywords】: Large-scale Negotiation, Interdependent Issues, Complex Contracts
【Paper Link】 【Pages】:1515-1521
【Authors】: Erik Cambria ; Daniel Olsher ; Dheeraj Rajagopal
【Abstract】: SenticNet is a publicly available semantic and affective resource for concept-level sentiment analysis. Rather than using graph-mining and dimensionality-reduction techniques, SenticNet 3 makes use of "energy flows" to connect various parts of extended common and common-sense knowledge representations to one another. SenticNet 3 models nuanced semantics and sentics (that is, the conceptual and affective information associated with multi-word natural language expressions), representing information with a symbolic opacity of an intermediate nature between that of neural networks and typical symbolic systems.
【Keywords】: SenticNet; concept-level sentiment analysis; biologically-inspired opinion mining; sentic computing; knowledge representation
【Paper Link】 【Pages】:1522-1528
【Authors】: Linfeng Song ; Yue Zhang ; Kai Song ; Qun Liu
【Abstract】: There has been growing interest in stochastic methods to natural language generation (NLG). While most NLG pipelines separate morphological generation and syntactic linearization, the two tasks are closely related. In this paper, we study joint morphological generation and linearization, making use of word order and inflections information for both tasks and reducing error propagation. Experiments show that the joint method significantly outperforms a strong pipelined baseline (by 1.1 BLEU points). It also achieves the best reported result on the Generation Challenge 2011 shared task.
【Keywords】:
【Paper Link】 【Pages】:1529-1536
【Authors】: Johannes Twiefel ; Timo Baumann ; Stefan Heinrich ; Stefan Wermter
【Abstract】: Automatic speech recognition (ASR) technology has been developed to such a level that off-the-shelf distributed speech recognition services are available (free of cost), which allow researchers to integrate speech into their applications with little development effort or expert knowledge leading to better results compared with previously used open-source tools. Often, however, such services do not accept language models or grammars but process free speech from any domain. While results are very good given the enormous size of the search space, results frequently contain out-of-domain words or constructs that cannot be understood by subsequent domain-dependent natural language understanding (NLU) components. We present a versatile post-processing technique based on phonetic distance that integrates domain knowledge with open-domain ASR results, leading to improved ASR performance. Notably, our technique is able to make use of domain restrictions using various degrees of domain knowledge, ranging from pure vocabulary restrictions via grammars or N-Grams to restrictions of the acceptable utterances. We present results for a variety of corpora (mainly from human-robot interaction) where our combined approach significantly outperforms Google ASR as well as a plain open-source ASR solution.
【Keywords】: speech recognition; domain knowledge; cloud-based knowledge; phonetic distance
【Paper Link】 【Pages】:1537-1543
【Authors】: Li Dong ; Furu Wei ; Ming Zhou ; Ke Xu
【Abstract】: Recursive neural models have achieved promising results in many natural language processing tasks. The main difference among these models lies in the composition function, i.e., how to obtain the vector representation for a phrase or sentence using the representations of words it contains. This paper introduces a novel Adaptive Multi-Compositionality (AdaMC) layer to recursive neural models. The basic idea is to use more than one composition functions and adaptively select them depending on the input vectors. We present a general framework to model each semantic composition as a distribution over these composition functions. The composition functions and parameters used for adaptive selection are learned jointly from data. We integrate AdaMC into existing recursive neural models and conduct extensive experiments on the Stanford Sentiment Treebank. The results illustrate that AdaMC significantly outperforms state-of-the-art sentiment classification methods. It helps push the best accuracy of sentence-level negative/positive classification from 85.4% up to 88.5%.
【Keywords】: recursive neural network; semantic composition; deep learning; sentiment classification
【Paper Link】 【Pages】:1544-1550
【Authors】: Rui Fang ; Malcolm Doering ; Joyce Y. Chai
【Abstract】: In situated dialogue with artificial agents (e.g., robots), although a human and an agent are co-present, the agent's representation and the human's representation of the shared environment are significantly mismatched. Because of this misalignment, our previous work has shown that when the agent applies traditional approaches to generate referring expressions for describing target objects with minimum descriptions, the intended objects often cannot be correctly identified by the human. To address this problem, motivated by collaborative behaviors in human referential communication, we have developed two collaborative models - an episodic model and an installment model - for referring expression generation. Both models, instead of generating a single referring expression to describe a target object as in the previous work, generate multiple small expressions that lead to the target object with the goal of minimizing the collaborative effort. In particular, our installment model incorporates human feedback in a reinforcement learning framework to learn the optimal generation strategies. Our empirical results have shown that the episodic model and the installment model outperform previous non-collaborative models with an absolute gain of 6% and 21% respectively.
【Keywords】: Collaborative Models;Referring Expression Generation; Situated Dialogue
【Paper Link】 【Pages】:1551-1557
【Authors】: Lionel Martin ; Pearl Pu
【Abstract】: Reviews keep playing an increasingly important role in the decision process of buying products and booking hotels. However, the large amount of available information can be confusing to users. A more succinct interface, gathering only the most helpful reviews, can reduce information processing time and save effort. To create such an interface in real time, we need reliable prediction algorithms to classify and predict new reviews which have not been voted but are potentially helpful. So far such helpfulness prediction algorithms have benefited from structural aspects, such as the length and readability score. Since emotional words are at the heart of our written communication and are powerful to trigger listeners' attention, we believe that emotional words can serve as important parameters for predicting helpfulness of review text. Using GALC, a general lexicon of emotional words associated with a model representing 20 different categories, we extracted the emotionality from the review text and applied supervised classification method to derive the emotion-based helpful review prediction. As the second contribution, we propose an evaluation framework comparing three different real-world datasets extracted from the most well-known product review websites. This framework shows that emotion-based methods are outperforming the structure-based approach, by up to 9%.
【Keywords】: Helpfulness prediction; Social Influence; Online product reviews
【Paper Link】 【Pages】:1558-1564
【Authors】: Iftekhar Naim ; Young Chol Song ; Qiguang Liu ; Henry A. Kautz ; Jiebo Luo ; Daniel Gildea
【Abstract】: We propose an unsupervised learning algorithm for automatically inferring the mappings between English nouns and corresponding video objects. Given a sequence of natural language instructions and an unaligned video recording, we simultaneously align each instruction to its corresponding video segment, and also align nouns in each instruction to their corresponding objects in video. While existing grounded language acquisition algorithms rely on pre-aligned supervised data (each sentence paired with corresponding image frame or video segment), our algorithm aims to automatically infer the alignment from the temporal structure of the video and parallel text instructions. We propose two generative models that are closely related to the HMM and IBM 1 word alignment models used in statistical machine translation. We evaluate our algorithm on videos of biological experiments performed in wetlabs, and demonstrate its capability of aligning video segments to text instructions and matching video objects to nouns in the absence of any direct supervision.
【Keywords】: Grounded Language Acquisition; Natural Language Processing; Language and Vision; Video Alignment; IBM models; HMM
【Paper Link】 【Pages】:1565-1571
【Authors】: John Walker Orr ; Prasad Tadepalli ; Janardhan Rao Doppa ; Xiaoli Fern ; Thomas G. Dietterich
【Abstract】: Scripts have been proposed to model the stereotypical event sequences found in narratives. They can be applied to make a variety of inferences including fillinggaps in the narratives and resolving ambiguous references. This paper proposes the first formal frameworkfor scripts based on Hidden Markov Models (HMMs). Our framework supports robust inference and learning algorithms, which are lacking in previous clustering models. We develop an algorithm for structure andparameter learning based on Expectation Maximizationand evaluate it on a number of natural datasets. The results show that our algorithm is superior to several informed baselines for predicting missing events in partialobservation sequences.
【Keywords】: hidden markov model; Script Learning; machine learning; graphical models; structure learning; natural language processing; expectation maximization; Structural EM; Missing Data
【Paper Link】 【Pages】:1572-1578
【Authors】: Lin Qiu ; Yong Cao ; Zaiqing Nie ; Yong Yu ; Yong Rui
【Abstract】: Distributed representations of words (aka word embedding) have proven helpful in solving natural language processing (NLP) tasks. Training distributed representations of words with neural networks has lately been a major focus of researchers in the field. Recent work on word embedding, the Continuous Bag-of-Words (CBOW) model and the Continuous Skip-gram (Skip-gram) model, have produced particularly impressive results, significantly speeding up the training process to enable word representation learning from large-scale data. However, both CBOW and Skip-gram do not pay enough attention to word proximity in terms of model or word ambiguity in terms of linguistics. In this paper, we propose Proximity-Ambiguity Sensitive (PAS) models (i.e. PAS CBOW and PAS Skip-gram) to produce high quality distributed representations of words considering both word proximity and ambiguity. From the model perspective, we introduce proximity weights as parameters to be learned in PAS CBOW and used in PAS Skip-gram. By better modeling word proximity, we reveal the strength of pooling-structured neural networks in word representation learning. The proximity-sensitive pooling layer can also be applied to other neural network applications that employ pooling layers. From the linguistics perspective, we train multiple representation vectors per word. Each representation vector corresponds to a particular group of POS tags of the word. By using PAS models, we achieved a 16.9% increase in accuracy over state-of-the-art models.
【Keywords】: Word Representation; Neural Networks
【Paper Link】 【Pages】:1579-1585
【Authors】: Yangqiu Song ; Dan Roth
【Abstract】: In this paper, we systematically study the problem of dataless hierarchical text classification. Unlike standard text classification schemes that rely on supervised training, dataless classification depends on understanding the labels of the sought after categories and requires no labeled data. Given a collection of text documents and a set of labels, we show that understanding the labels can be used to accurately categorize the documents. This is done by embedding both labels and documents in a semantic space that allows one to compute meaningful semantic similarity between a document and a potential label. We show that this scheme can be used to support accurate multiclass classification without any supervision. We study several semantic representations and show how to improve the classification using bootstrapping. Our results show that bootstrapped dataless classification is competitive with supervised classification with thousands of labeled examples.
【Keywords】: text classification; semantic representation; dataless classification
【Paper Link】 【Pages】:1586-1592
【Authors】: Alessandro Sordoni ; Yoshua Bengio ; Jian-Yun Nie
【Abstract】: In web search, users queries are formulated using only few terms and term-matching retrieval functions could fail at retrieving relevant documents. Given a user query, the technique of query expansion (QE) consists in selecting related terms that could enhance the likelihood of retrieving relevant documents. Selecting such expansion terms is challenging and requires a computational framework capable of encoding complex semantic relationships. In this paper, we propose a novel method for learning, in a supervised way, semantic representations for words and phrases. By embedding queries and documents in special matrices, our model disposes of an increased representational power with respect to existing approaches adopting a vector representation. We show that our model produces high-quality query expansion terms. Our expansion increase IR measures beyond expansion from current word-embeddings models and well-established traditional QE methods.
【Keywords】: query expansion; word embeddings;
【Paper Link】 【Pages】:1593-1599
【Authors】: Jun Suzuki ; Masaaki Nagata
【Abstract】: The automatic discovery of a significant low-dimensional feature representation from a given data set is a fundamental problem in machine learning. This paper focuses specifically on the development of the feature representation discovery methods appropriate for high-dimensional and sparse data. We formulate our feature representation discovery problem as a variant of the semi-supervised learning problem, namely, as an optimization problem over unsupervised data whose objective is evaluating the impact of each feature with respect to modeling a target task according to the initial model constructed by using supervised data. The most notable characteristic of our method is that it offers a feasible processing speed even if the numbers of data and features are both in the millions or even billions, and successfully provides a significantly small number of feature sets, i.e., fewer than 10, that can also offer improved performance compared with those obtained with the original feature sets. We demonstrate the effectiveness of our method in experiments consisting of two well-studied natural language processing tasks.
【Keywords】: Feature Representation Discovery; Semi-supervised Learning; Natural Language Processing
【Paper Link】 【Pages】:1600-1606
【Authors】: Rui Xia ; Jianfei Yu ; Feng Xu ; Shumei Wang
【Abstract】: In the field of NLP, most of the existing domain adaptation studies belong to the feature-based adaptation, while the research of instance-based adaptation is very scarce. In this work, we propose a new instance-based adaptation model, called in-target-domain logistic approximation (ILA). In ILA, we adapt the source-domain data to the target domain by a logistic approximation. The normalized in-target-domain probability is assigned as an instance weight to each of the source-domain training data. An instance-weighted classification model is trained finally for the cross-domain classification problem. Compared to the previous techniques, ILA conducts instance adaptation in a dimensionality-reduced linear feature space to ensure efficiency in high-dimensional NLP tasks. The instance weights in ILA are learnt by leveraging the criteria of both maximum likelihood and minimum statistical distance. The empirical results on two NLP tasks including text categorization and sentiment classification show that our ILA model beats the state-of-the-art instance adaptation methods significantly, in cross-domain classification accuracy, parameter stability and computational efficiency.
【Keywords】: domain adaptation; instance adaptation; transfer learning
【Paper Link】 【Pages】:1607-1614
【Authors】: Min Xiao ; Yuhong Guo
【Abstract】: Cross-lingual text classification is the task of assigning labels to observed documents in a label-scarce target language domain by using a prediction model trained with labeled documents from a label-rich source language domain. Cross-lingual text classification is popularly studied in natural language processing area to reduce the expensive manual annotation effort required in the target language domain. In this work, we propose a novel semi-supervised representation learning approach to address this challenging task by inducing interlingual features via semi-supervised matrix completion. To evaluate the proposed learning technique, we conduct extensive experiments on eighteen cross language sentiment classification tasks with four different languages. The empirical results demonstrate the efficacy of the proposed approach, and show it outperforms a number of related cross-lingual learning methods.
【Keywords】: cross-lingual text classification
【Paper Link】 【Pages】:1615-1621
【Authors】: Chen Chen ; Vincent Ng
【Abstract】: Much research has been done on the problem of English pronoun resolution, but there has been relatively little work on the corresponding problem of Chinese pronoun resolution. While pronoun resolution in both languages remains a challenging task, Chinese pronoun resolution is further complicated by (1) the lack of publicly available Chinese word lists or dictionaries that can be used to look up essential mention attributes such as gender and number; and (2) the relative dearth of Chinese coreference-annotated data. Existing approaches to Chinese pronoun resolution are monolingual, training and testing a pronoun resolver on Chinese data. In contrast, we propose a bilingual approach to Chinese pronoun resolution, aiming to improve the resolution of Chinese pronouns by leveraging the publicly available English dictionaries and coreference annotations. Experiments on the OntoNotes 5.0 corpus demonstrate that our bilingual approach to Chinese pronoun resolution significantly surpasses the performance of state-of-the-art monolingual approaches.
【Keywords】:
【Paper Link】 【Pages】:1622-1628
【Authors】: Chen Chen ; Vincent Ng
【Abstract】: State-of-the-art approaches to Chinese zero pronoun resolution are supervised, requiring training documents with manually resolved zero pronouns. To eliminate the reliance on annotated data, we propose an unsupervised approach to this task. Underlying our approach is the novel idea of employing a model trained on manually resolved overt pronouns to resolve zero pronouns. Experimental results on the OntoNotes 5.0 corpus are encouraging: our unsupervised model surpasses its supervised counterparts in performance.
【Keywords】:
【Paper Link】 【Pages】:1629-1635
【Authors】: Sujatha Das Gollapalli ; Cornelia Caragea
【Abstract】: Keyphrases for a document concisely describe the document using a small set of phrases. Keyphrases were previously shown to improve several document processing and retrieval tasks. In this work, we study keyphrase extraction from research papers by leveraging citation networks. We propose CiteTextRank for keyphrase extraction from research articles, a graph-based algorithm that incorporates evidence from both a document's content as well as the contexts in which the document is referenced within a citation network. Our model obtains significant improvements over the state-of-the-art models for this task. Specifically, on several datasets of research papers, CiteTextRank improves precision at rank 1 by as much as 9-20% over state-of-the-art baselines.
【Keywords】:
【Paper Link】 【Pages】:1636-1642
【Authors】: Fangtao Li ; Sheng Wang ; Shenghua Liu ; Ming Zhang
【Abstract】: Probabilistic topic models have been widely used for sentiment analysis. However, most of existing topic methods only model the sentiment text, but do not consider the user, who expresses the sentiment, and the item, which the sentiment is expressed on. Since different users may use different sentiment expressions for different items, we argue that it is better to incorporate the user and item information into the topic model for sentiment analysis. In this paper, we propose a new Supervised User-Item based Topic model, called SUIT model, for sentiment analysis. It can simultaneously utilize the textual topic and latent user-item factors. Our proposed method uses the tensor outer product of text topic proportion vector, user latent factor and item latent factor to model the sentiment label generalization. Extensive experiments are conducted on two datasets: review dataset and microblog dataset. The results demonstrate the advantages of our model. It shows significant improvement compared with supervised topic models and collaborative filtering methods.
【Keywords】:
【Paper Link】 【Pages】:1643-1649
【Authors】: Lizi Liao ; Jing Jiang ; Ying Ding ; Heyan Huang ; Ee-Peng Lim
【Abstract】: As the rapid growth of online social media attracts a large number of Internet users, the large volume of content generated by these users also provides us with an opportunity to study the lexical variation of people of different ages. In this paper, we present a latent variable model that jointly models the lexical content of tweets and Twitter users’ ages. Our model inherently assumes that a topic has not only a word distribution but also an age distribution. We propose a Gibbs-EM algorithm to perform inference on our model. Empirical evaluation shows that our model can learn meaningful age-specific topics such as “school” for teenagers and “health” for older people. Our model can also be used for age prediction and performs better than a number of baseline methods.
【Keywords】: Age topic model;Gibbs-EM;Lexical variation
【Paper Link】 【Pages】:1650-1656
【Authors】: Yinfei Yang ; Ani Nenkova
【Abstract】: We introduce the task of identifying information-dense texts,which report important factual information in direct, succinct manner. We describe a procedure that allows us to label automatically a large training corpus of New York Times texts.We train a classifier based on lexical, discourse and unlexicalized syntactic features and test its performance on a set of manually annotated articles from business, U.S. international relations, sports and science domains. Our results indicate that the task is feasible and that both syntactic and lexicalfeatures are highly predictive for the distinction. We observe considerable variation of prediction accuracy across domains and find that domain-specific models are more accurate.
【Keywords】: Summarization, Lead informativeness prediction, news analysis
【Paper Link】 【Pages】:1657-1664
【Authors】: Jiajun Zhang ; Shujie Liu ; Mu Li ; Ming Zhou ; Chengqing Zong
【Abstract】: The conventional statistical machine translation (SMT) methods perform the decoding process by compositing a set of the translation rules which are associated with high probabilities. However, the probabilities of the translation rules are calculated only according to the cooccurrence statistics in the bilingual corpus rather than the semantic meaning similarity. In this paper, we propose a Recursive Neural Network (RNN) based model that converts each translation rule into a compact real-valued vector in the semantic embedding space and performs the decoding process by minimizing the semantic gap between the source language string and its translation candidates at each state in a bottom-up structure. The RNN-based translation model is trained using a max-margin objective function. Extensive experiments on Chinese-to-English translation show that our RNN-based model can significantly improve the translation quality by up to 1.68 BLEU score.
【Keywords】:
【Paper Link】 【Pages】:1665-1672
【Authors】: Maruan Al-Shedivat ; Jim Jing-Yan Wang ; Majed Alzahrani ; Jianhua Z. Huang ; Xin Gao
【Abstract】: A combination of the sparse coding and transfer learning techniques was shown to be accurate and robust in classification tasks where training and testing objects have a shared feature space but are sampled from different underlying distributions, i.e., belong to different domains. The key assumption in such case is that in spite of the domain disparity, samples from different domains share some common hidden factors. Previous methods often assumed that all the objects in the target domain are unlabeled, and thus the training set solely comprised objects from the source domain. However, in real world applications, the target domain often has some labeled objects, or one can always manually label a small number of them. In this paper, we explore such possibility and show how a small number of labeled data in the target domain can significantly leverage classification accuracy of the state-of-the-art transfer sparse coding methods. We further propose a unified framework named supervised transfer sparse coding (STSC) which simultaneously optimizes sparse representation, domain transfer and classification. Experimental results on three applications demonstrate that a little manual labeling and then learning the model in a supervised fashion can significantly improve classification accuracy.
【Keywords】: Sparse coding; Transfer learning; Supervised learning; Classification; Support Vector Machine
【Paper Link】 【Pages】:1673-1679
【Authors】: Alnur Ali ; Rich Caruana ; Ashish Kapoor
【Abstract】: Most active learning methods avoid model selection by training models of one type (SVMs, boosted trees, etc.) using one pre-defined set of model hyperparameters. We propose an algorithm that actively samples data to simultaneously train a set of candidate models (different model types and/or different hyperparameters) and also select the best model from this set. The algorithm actively samples points for training that are most likely to improve the accuracy of the more promising candidate models, and also samples points for model selection---all samples count against the same labeling budget. This exposes a natural trade-off between the focused active sampling that is most effective for training models, and the unbiased sampling that is better for model selection. We empirically demonstrate on six test problems that this algorithm is nearly as effective as an active learning oracle that knows the optimal model in advance.
【Keywords】: machine learning; active learning; model selection
【Paper Link】 【Pages】:1680-1686
【Authors】: Wei Bi ; James T. Kwok
【Abstract】: Many real-world applications involve multilabel classification, in which the labels can have strong inter-dependencies and some of them may even be missing.Existing multilabel algorithms are unable to handle both issues simultaneously.In this paper, we propose a probabilistic model that can automatically learn and exploit multilabel correlations.By integrating out the missing information, it also provides a disciplinedapproach to the handling of missing labels. The inference procedure is simple, and the optimization subproblems are convex. Experiments on a number of real-world data sets with both complete and missing labelsdemonstrate that the proposed algorithm can consistently outperform state-of-the-art multilabel classification algorithms.
【Keywords】: multi-label classification;missing labels
【Paper Link】 【Pages】:1687-1693
【Authors】: Tim Brys ; Ann Nowé ; Daniel Kudenko ; Matthew E. Taylor
【Abstract】: Multi-objective problems with correlated objectives are a class of problems that deserve specific attention. In contrast to typical multi-objective problems, they do not require the identification of trade-offs between the objectives, as (near-) optimal solutions for any objective are (near-) optimal for every objective. Intelligently combining the feedback from these objectives, instead of only looking at a single one, can improve optimization. This class of problems is very relevant in reinforcement learning, as any single-objective reinforcement learning problem can be framed as such a multi-objective problem using multiple reward shaping functions. After discussing this problem class, we propose a solution technique for such reinforcement learning problems, called adaptive objective selection. This technique makes a temporal difference learner estimate the Q-function for each objective in parallel, and introduces a way of measuring confidence in these estimates. This confidence metric is then used to choose which objective's estimates to use for action selection. We show significant improvements in performance over other plausible techniques on two problem domains. Finally, we provide an intuitive analysis of the technique's decisions, yielding insights into the nature of the problems being solved.
【Keywords】: Reinforcement Learning; Multi-Objectivization; Ensemble Techniques
【Paper Link】 【Pages】:1694-1700
【Authors】: Kerstin Bunte ; Matti Järvisalo ; Jeremias Berg ; Petri Myllymäki ; Jaakko Peltonen ; Samuel Kaski
【Abstract】: We present a novel approach to low-dimensional neighbor embedding for visualization, based on formulating an information retrieval based neighborhood preservation cost function as Maximum satisfiability on a discretized output display. The method has a rigorous interpretation as optimal visualization based on the cost function. Unlike previous low-dimensional neighbor embedding methods, our formulation is guaranteed to yield globally optimal visualizations, and does so reasonably fast. Unlike previous manifold learning methods yielding global optima of their cost functions, our cost function and method are designed for low-dimensional visualization where evaluation and minimization of visualization errors are crucial. Our method performs well in experiments, yielding clean embeddings of datasets where a state-of-the-art comparison method yields poor arrangements. In a real-world case study for semi-supervised WLAN signal mapping in buildings we outperform state-of-the-art methods.
【Keywords】: Nonlinear dimensionality reduction, visualization, neighbor embedding, maximum satisfiability, constrained optimization
【Paper Link】 【Pages】:1701-1707
【Authors】: Róbert Busa-Fekete ; Balázs Szörényi ; Eyke Hüllermeier
【Abstract】: We introduce the problem of PAC rank elicitation, which consists of sorting a given set of options based on adaptive sampling of stochastic pairwise preferences. More specifically, we assume the existence of a ranking procedure, such as Copeland's method, that determines an underlying target order of the options. The goal is to predict a ranking that is sufficiently close to this target order with high probability, where closeness is measured in terms of a suitable distance measure. We instantiate this setting with combinations of two different distance measures and ranking procedures. For these instantiations, we devise efficient strategies for sampling pairwise preferences and analyze the corresponding sample complexity. We also present first experiments to illustrate the practical performance of our methods.
【Keywords】: rank elicitation, PAC, adaptive sampling, sample complexity
【Paper Link】 【Pages】:1708-1714
【Authors】: CJ Carey ; Sridhar Mahadevan
【Abstract】: Graph construction is the essential first step for nearly all manifold learning algorithms. While many applications assume that a simple k-nearest or epsilon-close neighbors graph will accurately model the topology of the underlying manifold, these methods often require expert tuning and may not produce high quality graphs. In this paper, the hyperparameter sensitivity of existing graph construction methods is demonstrated. We then present a new algorithm for unsupervised graph construction, based on minimal assumptions about the input data and its manifold structure.
【Keywords】: manifold learning; graph construction
【Paper Link】 【Pages】:1715-1721
【Authors】: Miguel Á. Carreira-Perpiñán ; Weiran Wang
【Abstract】: We consider the problem of learning soft assignments of N items to K categories given two sources of information: an item-category similarity matrix, which encourages items to be assigned to categories they are similar to (and to not be assigned to categories they are dissimilar to), and an item-item similarity matrix, which encourages similar items to have similar assignments. We propose a simple quadratic programming model that captures this intuition. We give necessary conditions for its solution to be unique, define an out-of-sample mapping, and derive a simple, effective training algorithm based on the alternating direction method of multipliers. The model predicts reasonable assignments from even a few similarity values, and can be seen as a generalization of semisupervised learning. It is particularly useful when items naturally belong to multiple categories, as for example when annotating documents with keywords or pictures with tags, with partially tagged items, or when the categories have complex interrelations (e.g. hierarchical) that are unknown.
【Keywords】: semi-supervised learning, convex optimization, ADMM, soft assignment, Laplacian smoothing
【Paper Link】 【Pages】:1722-1730
【Authors】: Supratik Chakraborty ; Daniel J. Fremont ; Kuldeep S. Meel ; Sanjit A. Seshia ; Moshe Y. Vardi
【Abstract】: Given a CNF formula and a weight for each assignment of values tovariables, two natural problems are weighted model counting anddistribution-aware sampling of satisfying assignments. Both problems have a wide variety of important applications. Due to the inherentcomplexity of the exact versions of the problems, interest has focusedon solving them approximately. Prior work in this area scaled only tosmall problems in practice, or failed to provide strong theoreticalguarantees, or employed a computationally-expensive most-probable-explanation ({\MPE}) queries that assumes prior knowledge of afactored representation of the weight distribution. We identify a novel parameter,\emph{tilt}, which is the ratio of the maximum weight of satisfying assignment to minimum weightof satisfying assignment and present anovel approach that works with a black-box oracle for weights ofassignments and requires only an {\NP}-oracle (in practice, a {\SAT}-solver) to solve both thecounting and sampling problems when the tilt is small. Our approach provides strong theoretical guarantees, and scales toproblems involving several thousand variables. We also show that theassumption of small tilt can be significantly relaxed while improving computational efficiency if a factored representation of the weights is known.
【Keywords】: Weighted model counting;weighted sampling;SAT;Probabilistic inference;machine learning
【Paper Link】 【Pages】:1731-1737
【Authors】: Sotirios Chatzis
【Abstract】: Collaborative filtering algorithms generally rely on the assumption that user preference patterns remain stationary. However, real-world relational data are seldom stationary. User preference patterns may change over time, giving rise to the requirement of designing collaborative filtering systems capable of detecting and adapting to preference pattern shifts. Motivated by this observation, in this paper we propose a dynamic Bayesian probabilistic matrix factorization model, designed for modeling time-varying distributions. Formulation of our model is based on imposition of a dynamic hierarchical Dirichlet process (dHDP) prior over the space of probabilistic matrix factorization models to capture the time-evolving statistical properties of modeled sequential relational datasets. We develop a simple Markov Chain Monte Carlo sampler to perform inference. We present experimental results to demonstrate the superiority of our temporal model.
【Keywords】: Probabilistic Matrix Factorization; Dynamic Hierarchical Dirichlet Process; Bayesian Nonparametrics; Collaborative Filtering
【Paper Link】 【Pages】:1738-1744
【Authors】: Sotirios Chatzis
【Abstract】: Restricted Boltzmann machines (RBMs) are a powerful generative modeling technique, based on a complex graphical model of hidden (latent) variables. Conditional RBMs (CRBMs) are an extension of RBMs tailored to modeling temporal data. A drawback of CRBMs is their consideration of linear temporal dependencies, which limits their capability to capture complex temporal structure. They also require many variables to model long temporal dependencies, a fact that might provoke overfitting proneness. To resolve these issues, in this paper we propose the echo-state CRBM (ES-CRBM): our model uses an echo-state network reservoir in the context of CRBMs to efficiently capture long and complex temporal dynamics, with much fewer trainable parameters compared to conventional CRBMs. In addition, we introduce an (implicit) mixture of ES-CRBM experts (im-ES-CRBM) to enhance even further the capabilities of our ES-CRBM model. The introduced im-ES-CRBM allows for better modeling temporal observations which might comprise a number of latent or observable subpatterns that alternate in a dynamic fashion. It also allows for performing sequence segmentation using our framework. We apply our methods to sequential data modeling and classification experiments using public datasets. As we show, our approach outperforms both existing RBM-based approaches as well as related state-of-the-art methods, such as conditional random fields.
【Keywords】: Conditional Restricted Boltzmann Machine; Echo-State Network; Contrastive Divergence
【Paper Link】 【Pages】:1745-1751
【Authors】: Dongdong Chen ; Jian Cheng Lv ; Zhang Yi
【Abstract】: The local neighborhood selection plays a crucial role for most representation based manifold learning algorithms. This paper reveals that an improper selection of neighborhood for learning representation will introduce negative components in the learnt representations. Importantly, the representations with negative components will affect the intrinsic manifold structure preservation. In this paper, a local non-negative pursuit (LNP) method is proposed for neighborhood selection and non-negative representations are learnt. Moreover, it is proved that the learnt representations are sparse and convex. Theoretical analysis and experimental results show that the proposed method achieves or outperforms the state-of-the-art results on various manifold learning problems.
【Keywords】: LNP; SCR; Sparse; Non-negative manifold learning
【Paper Link】 【Pages】:1752-1759
【Authors】: Ning Chen ; Jun Zhu ; Jianfei Chen ; Bo Zhang
【Abstract】: Dropout and other feature noising schemes have shown promising results in controlling over-fitting by artificially corrupting the training data. Though extensive theoretical and empirical studies have been performed for generalized linear models, little work has been done for support vector machines (SVMs), one of the most successful approaches for supervised learning. This paper presents dropout training for linear SVMs. To deal with the intractable expectation of the non-smooth hinge loss under corrupting distributions, we develop an iteratively re-weighted least square (IRLS) algorithm by exploring data augmentation techniques. Our algorithm iteratively minimizes the expectation of a re-weighted least square problem, where the re-weights have closed-form solutions. The similar ideas are applied to develop a new IRLS algorithm for the expected logistic loss under corrupting distributions. Our algorithms offer insights on the connection and difference between the hinge loss and logistic loss in dropout training. Empirical results on several real datasets demonstrate the effectiveness of dropout training on significantly boosting the classification accuracy of linear SVMs.
【Keywords】: Dropout Training; Support Vector Machines; Data Augmentation
【Paper Link】 【Pages】:1760-1766
【Authors】: Qing Da ; Yang Yu ; Zhi-Hua Zhou
【Abstract】: In many real-world applications of learning, the environment is open and changes gradually, which requires the learning system to have the ability of detecting and adapting to the changes. Class-incremental learning (C-IL) is an important and practical problem where data from unseen augmented classes are fed, but has not been studied well in the past. In C-IL, the system should beware of predicting instances from augmented classes as a seen class, and thus faces the challenge that no such instances were observed during training stage. In this paper, we tackle the challenge by using unlabeled data, which can be cheaply collected in many real-world applications. We propose the LACU framework as well as the LACU-SVM approach to learn the concept of seen classes while incorporating the structure presented in the unlabeled data, so that the misclassification risks among the seen classes as well as between the augmented and the seen classes are minimized simultaneously. Experiments on diverse datasets show the effectiveness of the proposed approach.
【Keywords】: augmented class; unlabeled data
【Paper Link】 【Pages】:1767-1773
【Authors】: William Dabney ; Philip S. Thomas
【Abstract】: In this paper we investigate the application of natural gradient descent to Bellman error based reinforcement learning algorithms. This combination is interesting because natural gradient descent is invariant to the parameterization of the value function. This invariance property means that natural gradient descent adapts its update directions to correct for poorly conditioned representations. We present and analyze quadratic and linear time natural temporal difference learning algorithms, and prove that they are covariant. We conclude with experiments which suggest that the natural algorithms can match or outperform their non-natural counterparts using linear function approximation, and drastically improve upon their non-natural counterparts when using non-linear function approximation.
【Keywords】: reinforcement learning;natural gradient;machine learning
【Paper Link】 【Pages】:1774-1780
【Authors】: Patrick Dallaire ; Philippe Giguère ; Brahim Chaib-draa
【Abstract】: This paper presents an extension of the cascading Indian buffet process (CIBP) intended to learning arbitrary directed acyclic graph structures as opposed to the CIBP, which is limited to purely layered structures. The extended cascading Indian buffet process (eCIBP) essentially consists in adding an extra sampling step to the CIBP to generate connections between non-consecutive layers. In the context of graphical model structure learning, the proposed approach allows learning structures having an unbounded number of hidden random variables and automatically selecting the model complexity. We evaluated the extended process on multivariate density estimation and structure identification tasks by measuring the structure complexity and predictive performance. The results suggest the extension leads to extracting simpler graphs without scarifying predictive precision.
【Keywords】: Structure learning; Bayesian Learning; Bayesian nonparametrics; Graphical models; Belief networks; Infinite directed acyclic graphs
【Paper Link】 【Pages】:1781-1787
【Authors】: Hu Ding ; Jinhui Xu
【Abstract】: In this paper, we study a prototype learning problem, called Median Point-Set, whose objective is to construct a prototype for a set of given point-sets so as to minimize the total Earth Mover's Distances (EMD) between the prototype and the point-sets, where EMD between two point-sets is measured under affine transformation. For this problem, we present the first purely geometric approach. Comparing to existing graph-based approaches (e.g., median graph, shock graph), our approach has several unique advantages: (1) No encoding and decoding procedures are needed to map between objects and graphs, and therefore avoid errors caused by information losing during the mappings; (2) Staying only in the geometric domain makes our approach computationally more efficient and robust to noise. We evaluate the performance of our technique for prototype reconstruction on a random dataset and a benchmark dataset, handwriting Chinese characters. Experiments suggest that our technique considerably outperforms the existing graph-based methods.
【Keywords】: prototype learning; earth mover's distance; affine transformation; object recognition;
【Paper Link】 【Pages】:1788-1794
【Authors】: Nemanja Djuric ; Mihajlo Grbovic ; Vladan Radosavljevic ; Narayan Bhamidipati ; Slobodan Vucetic
【Abstract】: We consider the problem of personalization of online services from the viewpoint of ad targeting, where we seek to find the best ad categories to be shown to each user, resulting in improved user experience and increased advertiser's revenue. We propose to address this problem as a task of ranking the ad categories depending on a user's preference, and introduce a novel label ranking approach capable of efficiently learning non-linear, highly accurate models in large-scale settings. Experiments on real-world advertising data set with more than 3.2 million users show that the proposed algorithm outperforms the existing solutions in terms of both rank loss and top-K retrieval performance, strongly suggesting the benefit of using the proposed model on large-scale ranking problems.
【Keywords】: computational advertising; ad targeting; label ranking; large-scale learning; non-linear models
【Paper Link】 【Pages】:1795-1801
【Authors】: Janardhan Rao Doppa ; Jun Yu ; Chao Ma ; Alan Fern ; Prasad Tadepalli
【Abstract】: Multi-label learning concerns learning multiple, overlapping, and correlated classes. In this paper, we adapt a recent structured prediction framework called HC-Search for multi-label prediction problems. One of the main advantages of this framework is that its training is sensitive to the loss function, unlike the other multi-label approaches that either assume a specific loss function or require a manual adaptation to each loss function. We empirically evaluate our instantiation of the HC-Search framework along with many existing multi-label learning algorithms on a variety of benchmarks by employing diverse task loss functions. Our results demonstrate that the performance of existing algorithms tends to be very similar in most cases, and that the HC-Search approach is comparable and often better than all the other algorithms across different loss functions.
【Keywords】: Multi-Label Classification; Structured Prediction; Rank Learning; Learning for Search
【Paper Link】 【Pages】:1802-1808
【Authors】: Gary Doran ; Soumya Ray
【Abstract】: We analyze and evaluate a generative process for multiple-instance learning (MIL) in which bags are distributions over instances. We show that our generative process contains as special cases generative models explored in prior work, while excluding scenarios known to be hard for MIL. Further, under the mild assumption that every negative instance is observed with nonzero probability in some negative bag, we show that it is possible to learn concepts that accurately label instances from MI data in this setting. Finally, we show that standard supervised approaches can learn concepts with low area-under-ROC error from MI data in this setting. We validate this surprising result with experiments using several synthetic and real-world MI datasets that have been annotated with instance labels.
【Keywords】: multiple-instance learning; classification; learning theory
【Paper Link】 【Pages】:1809-1815
【Authors】: Meng Fang ; Jie Yin ; Dacheng Tao
【Abstract】: This paper studies the active learning problem in crowdsourcing settings, where multiple imperfect annotators with varying levels of expertise are available for labeling the data in a given task. Annotations collected from these labelers may be noisy and unreliable, and the quality of labeled data needs to be maintained for data mining tasks. Previous solutions have attempted to estimate individual users' reliability based on existing knowledge in each task, but for this to be effective each task requires a large quantity of labeled data to provide accurate estimates. In practice, annotation budgets for a given task are limited, so each instance can be presented to only a few users, each of whom can only label a few examples. To overcome data scarcity we propose a new probabilistic model that transfers knowledge from abundant unlabeled data in auxiliary domains to help estimate labelers' expertise. Based on this model we present a novel active learning algorithm that: a) simultaneously selects the most informative example and b) queries its label from the labeler with the best expertise. Experiments on both text and image datasets demonstrate that our proposed method outperforms other state-of-the-art active learning methods.
【Keywords】: Active Learning; Crowdsourcing; Knowledge Transfer
【Paper Link】 【Pages】:1816-1823
【Authors】: Victor Gabillon ; Branislav Kveton ; Zheng Wen ; Brian Eriksson ; S. Muthukrishnan
【Abstract】: Maximization of submodular functions has wide applications in artificial intelligence and machine learning. In this paper, we propose a scalable learning algorithm for maximizing an adaptive submodular function. The key structural assumption in our solution is that the state of each item is distributed according to a generalized linear model, which is conditioned on the feature vector of the item. Our objective is to learn the parameters of this model. We analyze the performance of our algorithm, and show that its regret is polylogarithmic in time and linear in the number of features. Finally, we evaluate our solution on two problems, preference elicitation and adaptive face detection, and demonstrate that high-quality policies can be learned sample efficiently.
【Keywords】: Online Learning;Active Learning; Bandits; Submodularity;Generalized Linear Models;
【Paper Link】 【Pages】:1824-1830
【Authors】: Robby Goetschalckx ; Alan Fern ; Prasad Tadepalli
【Abstract】: Coactive learning is an online problem solving setting where the solutions provided by a solver are interactively improved by a domain expert, which in turn drives learning. In this paper we extend the study of coactive learning to problems where obtaining a globally optimal or near-optimal solution may be intractable or where an expert can only be expected to make small, local improvements to a candidate solution. The goal of learning in this new setting is to minimize the cost as measured by the expert effort over time. We first establish theoretical bounds on the average cost of the existing coactive Perceptron algorithm. In addition, we consider new online algorithms that use cost-sensitive and Passive-Aggressive (PA) updates, showing similar or improved theoretical bounds. We provide an empirical evaluation of the learners in various domains, which show that the Perceptron based algorithms are quite effective and that unlike the case for online classification, the PA algorithms do not yield significant performance gains.
【Keywords】: Coactive Learning; locally optimal planning
【Paper Link】 【Pages】:1831-1839
【Authors】: Mehmet Gönen ; Adam A. Margolin
【Abstract】: Transfer learning considers related but distinct tasks defined on heterogenous domains and tries to transfer knowledge between these tasks to improve generalization performance. It is particularly useful when we do not have sufficient amount of labeled training data in some tasks, which may be very costly, laborious, or even infeasible to obtain. Instead, learning the tasks jointly enables us to effectively increase the amount of labeled training data. In this paper, we formulate a kernelized Bayesian transfer learning framework that is a principled combination of kernel-based dimensionality reduction models with task-specific projection matrices to find a shared subspace and a coupled classification model for all of the tasks in this subspace. Our two main contributions are: (i) two novel probabilistic models for binary and multiclass classification, and (ii) very efficient variational approximation procedures for these models. We illustrate the generalization performance of our algorithms on two different applications. In computer vision experiments, our method outperforms the state-of-the-art algorithms on nine out of 12 benchmark supervised domain adaptation experiments defined on two object recognition data sets. In cancer biology experiments, we use our algorithm to predict mutation status of important cancer genes from gene expression profiles using two distinct cancer populations, namely, patient-derived primary tumor data and in-vitro-derived cancer cell line data. We show that we can increase our generalization performance on primary tumors using cell lines as an auxiliary data source.
【Keywords】: transfer learning; domain adaptation; cross-domain learning; kernel methods; variational approximation
【Paper Link】 【Pages】:1840-1846
【Authors】: Chen Gong ; Dacheng Tao ; Keren Fu ; Jie Yang
【Abstract】: The smoothness hypothesis is critical for graph-based semi-supervised learning. This paper defines local smoothness, based on which a new algorithm, Reliable Label Inference via Smoothness Hypothesis (ReLISH), is proposed. ReLISH has produced smoother labels than some existing methods for both labeled and unlabeled examples. Theoretical analyses demonstrate good stability and generalizability of ReLISH. Using real-world datasets, our empirical analyses reveal that ReLISH is promising for both transductive and inductive tasks, when compared with representative algorithms, including Harmonic Functions, Local and Global Consistency, Constraint Metric Learning, Linear Neighborhood Propagation, and Manifold Regularization.
【Keywords】: Semi-supervised learning; Local smoothness; Regularization
【Paper Link】 【Pages】:1847-1853
【Authors】: Chen Gong ; Dacheng Tao ; Jie Yang ; Keren Fu
【Abstract】: Manifold learning is a powerful tool for solving nonlinear dimension reduction problems. By assuming that the high-dimensional data usually lie on a low-dimensional manifold, many algorithms have been proposed. However, most algorithms simply adopt the traditional graph Laplacian to encode the data locality, so the discriminative ability is limited and the embedding results are not always suitable for the subsequent classification. Instead, this paper deploys the signed graph Laplacian and proposes Signed Laplacian Embedding (SLE) for supervised dimension reduction. By exploring the label information, SLE comprehensively transfers the discrimination carried by the original data to the embedded low-dimensional space. Without perturbing the discrimination structure, SLE also retains the locality.Theoretically, we prove the immersion property by computing the rank of projection, and relate SLE to existing algorithms in the frame of patch alignment. Thorough empirical studies on synthetic and real datasets demonstrate the effectiveness of SLE.
【Keywords】: Dimension reduction; Manifold learning; Signed graph Laplacian
【Paper Link】 【Pages】:1854-1860
【Authors】: Lei Han ; Yu Zhang ; Guojie Song ; Kunqing Xie
【Abstract】: Multi-task learning seeks to improve the generalization performance by sharing common information among multiple related tasks. A key assumption in most MTL algorithms is that all tasks are related, which, however, may not hold in many real-world applications. Existing techniques, which attempt to address this issue, aim to identify groups of related tasks using group sparsity. In this paper, we propose a probabilistic tree sparsity (PTS) model to utilize the tree structure to obtain the sparse solution instead of the group structure. Specifically, each model coefficient in the learning model is decomposed into a product of multiple component coefficients each of which corresponds to a node in the tree. Based on the decomposition, Gaussian and Cauchy distributions are placed on the component coefficients as priors to restrict the model complexity. We devise an efficient expectation maximization algorithm to learn the model parameters. Experiments conducted on both synthetic and real-world problems show the effectiveness of our model compared with state-of-the-art baselines.
【Keywords】: Multi-Task Learning; Sparsity; Probabilistic Modeling
【Paper Link】 【Pages】:1861-1867
【Authors】: Liang Hu ; Jian Cao ; Guandong Xu ; Longbing Cao ; Zhiping Gu ; Wei Cao
【Abstract】: Nowadays, most recommender systems (RSs) mainly aim to suggest appropriate items for individuals. Due to the social nature of human beings, group activities have become an integral part of our daily life, thus motivating the study on group RS (GRS). However, most existing methods used by GRS make recommendations through aggregating individual ratings or individual predictive results rather than considering the collective features that govern user choices made within a group. As a result, such methods are heavily sensitive to data, hence they often fail to learn group preferences when the data are slightly inconsistent with predefined aggregation assumptions. To this end, we devise a novel GRS approach which accommodates both individual choices and group decisions in a joint model. More specifically, we propose a deep-architecture model built with collective deep belief networks and dual-wing restricted Boltzmann machines. With such a deep model, we can use high-level features, which are induced from lower-level features, to represent group preference so as to relieve the vulnerability of data. Finally, the experiments conducted on a real-world dataset prove the superiority of our deep model over other state-of-the-art methods.
【Keywords】: group recommender system; feature learning; deep learning; restricted boltzmann machine; deep belief net
【Paper Link】 【Pages】:1868-1874
【Authors】: Sheng-Jun Huang ; Wei Gao ; Zhi-Hua Zhou
【Abstract】: In multi-instance multi-label learning (MIML), one object is represented by multiple instances and simultaneously associated with multiple labels. Existing MIML approaches have been found useful in many applications; however, most of them can only handle moderate-sized data. To efficiently handle large data sets, we propose the MIMLfast approach, which first constructs a low-dimensional subspace shared by all labels, and then trains label specific linear models to optimize approximated ranking loss via stochastic gradient descent. Although the MIML problem is complicated, MIMLfast is able to achieve excellent performance by exploiting label relations with shared space and discovering sub-concepts for complicated labels. Experiments show that the performance of MIMLfast is highly competitive to state-of-the-art techniques, whereas its time cost is much less; particularly, on a data set with 30K bags and 270K instances, where none of existing approaches can return results in 24 hours, MIMLfast takes only 12 minutes. Moreover, our approach is able to identify the most representative instance for each label, and thus providing a chance to understand the relation between input patterns and output semantics.
【Keywords】: multi-instance multi-label learning; fast; key instance
【Paper Link】 【Pages】:1875-1881
【Authors】: Vahid Jalali ; David Leake
【Abstract】: In case-based reasoning (CBR), problems are solved by retrieving prior cases and adapting their solutions to fit; learning occurs as new cases are stored. Controlling the growth of the case base is a fundamental problem, and research on case-base maintenance has developed methods for compacting case bases while maintaining system competence, primarily by competence-based deletion strategies assuming static case adaptation knowledge. This paper proposes adaptation-guided case-base maintenance (AGCBM), a case-base maintenance approach exploiting the ability to dynamically generate new adaptation knowledge from cases. In AGCBM, case retention decisions are based both on cases' value as base cases for solving problems and on their value for generating new adaptation rules. he paper illustrates the method for numerical prediction tasks (case-based regression) in which adaptation rules are generated automatically using the case difference heuristic. In comparisons of AGCBM to five alternative methods in four domains, for varying case base densities, AGCBM outperformed the alternatives in all domains, with greatest benefit at high compression.
【Keywords】: Adaptation-Guided Case Base Maintenance; Case Base Maintenance; Case-Based Reasoning
【Paper Link】 【Pages】:1882-1889
【Authors】: Xiao-Yuan Jing ; Ruimin Hu ; Yang-Ping Zhu ; Shanshan Wu ; Chao Liang ; Jing-Yu Yang
【Abstract】: Multi-view feature learning is an attractive research topic with great practical success. Canonical correlation analysis (CCA) has become an important technique in multi-view learning, since it can fully utilize the inter-view correlation. In this paper, we mainly study the CCA based multi-view supervised feature learning technique where the labels of training samples are known. Several supervised CCA based multi-view methods have been presented, which focus on investigating the supervised correlation across different views. However, they take no account of the intra-view correlation between samples. Researchers have also introduced the discriminant analysis technique into multi-view feature learning, such as multi-view discriminant analysis (MvDA). But they ignore the canonical correlation within each view and between all views. In this paper, we propose a novel multi-view feature learning approach based on intra-view and inter-view supervised correlation analysis (I2SCA), which can explore the useful correlation information of samples within each view and between all views. The objective function of I2SCA is designed to simultaneously extract the discriminatingly correlated features from both inter-view and intra-view. It can obtain an analytical solution without iterative calculation. And we provide a kernelized extension of I2SCA to tackle the linearly inseparable problem in the original feature space. Four widely-used datasets are employed as test data. Experimental results demonstrate that our proposed approaches outperform several representative multi-view supervised feature learning methods.
【Keywords】: Canonical correlation analysis (CCA); Multi-view supervised feature learning; Inter-view and intra-view supervised correlation analysis; Analytical solution; Kernelized extension
【Paper Link】 【Pages】:1890-1896
【Authors】: Kshitij Judah ; Alan Paul Fern ; Prasad Tadepalli ; Robby Goetschalckx
【Abstract】: Imitation Learning (IL) is a popular approach for teaching behavior policies to agents by demonstrating the desired target policy. While the approach has lead to many successes, IL often requires a large set of demonstrations to achieve robust learning, which can be expensive for the teacher. In this paper, we consider a novel approach to improve the learning efficiency of IL by providing a shaping reward function in addition to the usual demonstrations. Shaping rewards are numeric functions of states (and possibly actions) that are generally easily specified, and capture general principles of desired behavior, without necessarily completely specifying the behavior. Shaping rewards have been used extensively in reinforcement learning, but have been seldom considered for IL, though they are often easy to specify. Our main contribution is to propose an IL approach that learns from both shaping rewards and demonstrations. We demonstrate the effectiveness of the approach across several IL problems, even when the shaping reward is not fully consistent with the demonstrations.
【Keywords】: Sequential Decision Making; Imitation Learning; Reinforcement Learning; Reward Shaping
【Paper Link】 【Pages】:1897-1903
【Authors】: Motonobu Kanagawa ; Yu Nishiyama ; Arthur Gretton ; Kenji Fukumizu
【Abstract】: Recent advances of kernel methods have yielded a framework for representing probabilities using a reproducing kernel Hilbert space, called kernel embedding of distributions. In this paper, we propose a Monte Carlo filtering algorithm based on kernel embeddings. The proposed method is applied to state-space models where sampling from the transition model is possible, while the observation model is to be learned from training samples without assuming a parametric model. As a theoretical basis of the proposed method, we prove consistency of the Monte Carlo method combined with kernel embeddings. Experimental results on synthetic models and real vision-based robot localization confirm the effectiveness of the proposed approach.
【Keywords】:
【Paper Link】 【Pages】:1904-1910
【Authors】: Kristian Kersting ; Martin Mladenov ; Roman Garnett ; Martin Grohe
【Abstract】: Color refinement is a basic algorithmic routine for graph isomorphismtesting and has recently been used for computing graph kernels as well as for lifting belief propagation and linear programming. So far, color refinement has been treated as a combinatorial problem. Instead, we treat it as a nonlinear continuous optimization problem and prove thatit implements a conditional gradient optimizer that can be turned into graph clustering approaches using hashing and truncated power iterations. This shows that color refinement is easy to understand in terms of random walks, easy to implement (matrix-matrix/vector multiplications) and readily parallelizable. We support our theoretical results with experiments on real-world graphs with millions of edges.
【Keywords】: Fractional Automorphisms; Weisfeiler-Lehman; Conditional Gradient; Hashing; Power Iteration; Lifted Inference; Graph Kernels; Matrix-Vector Multiplication
【Paper Link】 【Pages】:1911-1917
【Authors】: Tomás Kocák ; Michal Valko ; Rémi Munos ; Shipra Agrawal
【Abstract】: Thompson Sampling (TS) has surged a lot of interest due to its good empirical performance, in particular in the computational advertising. Though successful, the tools for its performance analysis appeared only recently. In this paper, we describe and analyze SpectralTS algorithm for a bandit problem, where the payoffs of the choices are smooth given an underlying graph. In this setting, each choice is a node of a graph and the expected payoffs of the neighboring nodes are assumed to be similar. Although the setting has application both in recommender systems and advertising, the traditional algorithms would scale poorly with the number of choices. For that purpose we consider an effective dimension d, which is small in real-world graphs. We deliver the analysis showing that the regret of SpectralTS scales as d\sqrt(T \ln N) with high probability, where T is the time horizon and N is the number of choices. Since a d\sqrt(T \ln N) regret is comparable to the known results, SpectralTS offers a computationally more efficient alternative. We also show that our algorithm is competitive on both synthetic and real-world data.
【Keywords】: spectral bandits;thompson sampling;smooth functions on graphs
【Paper Link】 【Pages】:1918-1924
【Authors】: Deguang Kong ; Chris H. Q. Ding
【Abstract】: We present a feature selection method for solving sparse regularization problem, which hasa composite regularization of $\ellp$ norm and $\ell{\infty}$ norm.We use proximal gradient method to solve this \L1inf operator problem, where a simple but efficient algorithm is designed to minimize a relatively simple objective function, which contains a vector of $\ell2$ norm and $\ell\infty$ norm. Proposed method brings some insight for solving sparsity-favoring norm, andextensive experiments are conducted to characterize the effect of varying $p$ and to compare with other approaches on real world multi-class and multi-label datasets.
【Keywords】: structure sparsity; L_p; non-convex
【Paper Link】 【Pages】:1925-1931
【Authors】: Deguang Kong ; Chris H. Q. Ding
【Abstract】: In machine learning, linear discriminant analysis (LDA) is a popular dimension reduction method. In this paper, we first provide a new perspective of LDA from an information theory perspective. From this new perspective, we propose a new formulation of LDA, which uses the pairwise averaged class covariance instead of theglobally averaged class covariance used in standard LDA. This pairwise (averaged) covariance describes data distribution more accurately. The new perspective also provides a natural way to properly weigh different pairwise distances, which emphasizes the pairs of class with small distances, and this leads to the proposed pairwise covariance properly weighted LDA (pcLDA). The kernel version of pcLDA is presented to handle nonlinear projections. Efficient algorithms are presented to efficiently compute the proposed models.
【Keywords】: LDA; covariance; trace of ratio
【Paper Link】 【Pages】:1932-1938
【Authors】: George Konidaris ; Leslie Pack Kaelbling ; Tomás Lozano-Pérez
【Abstract】: We consider the problem of constructing a symbolic description of a continuous, low-level environment for use in planning. We show that symbols that can represent the preconditions and effects of an agent's actions are both necessary and sufficient for high-level planning. This eliminates the symbol design problem when a representation must be constructed in advance, and in principle enables an agent to autonomously learn its own symbolic representations. The resulting representation can be converted into PDDL, a canonical high-level planning representation that enables very fast planning.
【Keywords】: Learning; Planning; Reinforcement Learning; Representation
【Paper Link】 【Pages】:1939-1945
【Authors】: Matt J. Kusner ; Wenlin Chen ; Quan Zhou ; Zhixiang Eddie Xu ; Kilian Q. Weinberger ; Yixin Chen
【Abstract】: During the past decade, machine learning algorithms have become commonplace in large-scale real-world industrial applications. In these settings, the computation time to train and test machine learning algorithms is a key consideration. At training-time the algorithms must scale to very large data set sizes.At testing-time, the cost of feature extraction can dominate the CPU runtime. Recently, a promising method was proposed to account for the feature extraction cost at testing time, called Cost-sensitive Tree of Classifiers (CSTC). Although the CSTC problem is NP-hard, the authors suggest an approximation through a mixed-norm relaxation across many classifiers. This relaxation is slow to train and requires involved optimization hyperparameter tuning. We propose a different relaxation using approximate submodularity, called Approximately Submodular Tree of Classifiers (ASTC). ASTC is much simpler to implement, yields equivalent results but requires no optimization hyperparameter tuning and is up to two orders of magnitude faster to train.
【Keywords】: submodular optimization; feature-cost sensitive learning; budgeted learning; tree-based learning
【Paper Link】 【Pages】:1946-1952
【Authors】: Anastasios T. Kyrillidis ; Rabeeh Karimi Mahabadi ; Quoc Tran-Dinh ; Volkan Cevher
【Abstract】: We consider the class of convex minimization problems, composed of a self-concordant function, such as the logdet metric, a convex data fidelity term h(.) and, a regularizing — possibly non-smooth — function g(.). This type of problems have recently attracted a great deal of interest, mainly due to their omnipresence in top-notch applications. Under this locally Lipschitz continuous gradient setting, we analyze the convergence behavior of proximal Newton schemes with the added twist of a probable presence of inexact evaluations. We prove attractive convergence rate guarantees and enhance state-of-the-art optimization schemes to accommodate such developments. Experimental results on sparse covariance estimation show the merits of our algorithm, both in terms of recovery efficiency and complexity.
【Keywords】: Inexact proximal Newton methods; Sparse covariance estimation; Self-concordance property
【Paper Link】 【Pages】:1953-1959
【Authors】: Shiwei Lan ; Jeffrey Streets ; Babak Shahbaba
【Abstract】: In machine learning and statistics, probabilistic inference involving multimodal distributions is quite difficult. This is especially true in high dimensional problems, where most existing algorithms cannot easily move from one mode to another. To address this issue, we propose a novel Bayesian inference approach based on Markov Chain Monte Carlo. Our method can effectively sample from multimodal distributions, especially when the dimension is high and the modes are isolated. To this end, it exploits and modifies the Riemannian geometric properties of the target distribution to create \emph{wormholes} connecting modes in order to facilitate moving between them. Further, our proposed method uses the regeneration technique in order to adapt the algorithm by identifying new modes and updating the network of wormholes without affecting the stationary distribution. To find new modes, as opposed to rediscovering those previously identified, we employ a novel mode searching algorithm that explores a \emph{residual energy} function obtained by subtracting an approximate Gaussian mixture density (based on previously discovered modes) from the target density function.
【Keywords】: Multimodal distributions, Markov Chain Monte Carlo
【Paper Link】 【Pages】:1960-1967
【Authors】: Tuan M. V. Le ; Hady Wirawan Lauw
【Abstract】: Classical approaches to visualization directly reduce a document's high-dimensional representation into visualizable two or three dimensions, using techniques such as multidimensional scaling. More recent approaches consider an intermediate representation in topic space, between word space and visualization space, which preserves the semantics by topic modeling. We call the latter semantic visualization problem, as it seeks to jointly model topic and visualization. While previous approaches aim to preserve the global consistency, they do not consider the local consistency in terms of the intrinsic geometric structure of the document manifold. We therefore propose an unsupervised probabilistic model, called Semafore, which aims to preserve the manifold in the lower-dimensional spaces. Comprehensive experiments on several real-life text datasets of news articles and web pages show that Semafore significantly outperforms the state-of-the-art baselines on objective evaluation metrics.
【Keywords】: visualization; topic modeling; manifold; regularization
【Paper Link】 【Pages】:1968-1974
【Authors】: Shao-Yuan Li ; Yuan Jiang ; Zhi-Hua Zhou
【Abstract】: Real data are often with multiple modalities or comingfrom multiple channels, while multi-view clusteringprovides a natural formulation for generating clustersfrom such data. Previous studies assumed that each exampleappears in all views, or at least there is one viewcontaining all examples. In real tasks, however, it is oftenthe case that every view suffers from the missing ofsome data and therefore results in many partial examples,i.e., examples with some views missing. In this paper,we present possibly the first study on partial multiviewclustering. Our proposed approach, PVC, worksby establishing a latent subspace where the instancescorresponding to the same example in different viewsare close to each other, and similar instances (belongingto different examples) in the same view should bewell grouped. Experiments on two-view data demonstratethe advantages of our proposed approach.
【Keywords】:
【Paper Link】 【Pages】:1975-1981
【Authors】: Xinwang Liu ; Lei Wang ; Jian Zhang ; Jianping Yin
【Abstract】: Existing multiple kernel learning (MKL) algorithms \textit{indiscriminately} apply a same set of kernel combination weights to all samples. However, the utility of base kernels could vary across samples and a base kernel useful for one sample could become noisy for another. In this case, rigidly applying a same set of kernel combination weights could adversely affect the learning performance. To improve this situation, we propose a sample-adaptive MKL algorithm, in which base kernels are allowed to be adaptively switched on/off with respect to each sample. We achieve this goal by assigning a latent binary variable to each base kernel when it is applied to a sample. The kernel combination weights and the latent variables are jointly optimized via margin maximization principle. As demonstrated on five benchmark data sets, the proposed algorithm consistently outperforms the comparable ones in the literature.
【Keywords】: Kernel Methods, Max-Margin, Latent Variables Learning
【Paper Link】 【Pages】:1982-1988
【Authors】: Yong Luo ; Jian Tang ; Jun Yan ; Chao Xu ; Zheng Chen
【Abstract】: Word embedding aims to learn a continuous representation for each word. It attracts increasing attention due to its effectiveness in various tasks such as named entity recognition and language modeling. Most existing word embedding results are generally trained on one individual data source such as news pages or Wikipedia articles. However, when we apply them to other tasks such as web search, the performance suffers. To obtain a robust word embedding for different applications, multiple data sources could be leveraged. In this paper, we proposed a two-side multimodal neural network to learn a robust word embedding from multiple data sources including free text, user search queries and search click-through data. This framework takes the word embeddings learned from different data sources as pre-train, and then uses a two-side neural network to unify these embeddings. The pre-trained embeddings are obtained by adapting the recently proposed CBOW algorithm. Since the proposed neural network does not need to re-train word embeddings for a new task, it is highly scalable in real world problem solving. Besides, the network allows weighting different sources differently when applied to different application tasks. Experiments on two real-world applications including web search ranking and word similarity measuring show that our neural network with multiple sources outperforms state-of-the-art word embedding algorithm with each individual source. It also outperforms other competitive baselines using multiple sources.
【Keywords】: pre-train; word embedding; multiple sources; neural network
【Paper Link】 【Pages】:1989-1996
【Authors】: Farzaneh Mirzazadeh ; Yuhong Guo ; Dale Schuurmans
【Abstract】: We present a general framework for association learning, where entities are embedded in a common latent space to express relatedness by geometry -- an approach that underlies the state of the art for link prediction, relation learning, multi-label tagging, relevance retrieval and ranking. Although current approaches rely on local training applied to non-convex formulations, we demonstrate how general convex formulations can be achieved for entity embedding, both for standard multi-linear and prototype-distance models. We investigate an efficient optimization strategy that allows scaling. An experimental evaluation reveals the advantages of global training in different case studies.
【Keywords】: Convex; Co-embedding
【Paper Link】 【Pages】:1997-2003
【Authors】: Tetsuro Morimura ; Takayuki Osogami ; Tomoyuki Shirai
【Abstract】: Policy gradient reinforcement learning (PGRL) has been receiving substantial attention as a mean for seeking stochastic policies that maximize cumulative reward. However, the learning speed of PGRL is known to decrease substantially when PGRL explores the policies that give the Markov chains having long mixing time. We study a new approach of regularizing how the PGRL explores the policies by the use of the hitting time of the Markov chains. The hitting time gives an upper bound on the mixing time, and the proposed approach improves the learning efficiency by keeping the mixing time of the Markov chains short. In particular, we propose a method of temporal-difference learning for estimating the gradient of the hitting time. Numerical experiments show that the proposed method outperforms conventional methods of PGRL.
【Keywords】: reinforcement learning, Markov decision process, mixing time
【Paper Link】 【Pages】:2004-2012
【Authors】: Makoto Nakatsuji ; Yasuhiro Fujiwara ; Hiroyuki Toda ; Hiroshi Sawada ; Jinguang Zheng ; James Alexander Hendler
【Abstract】: Predicting human activities is important for improving recommender systems or analyzing social relationships among users. Those human activities are usually repre- sented as multi-object relationships (e.g. user’s tagging activities for items or user’s tweeting activities at some locations). Since multi-object relationships are naturally represented as a tensor, tensor factorization is becom- ing more important for predicting users’ possible ac- tivities. However, its prediction accuracy is weak for ambiguous and/or sparsely observed objects. Our so- lution, Semantic data Representation for Tensor Fac- torization (SRTF), tackles these problems by incorpo- rating semantics into tensor factorization based on the following ideas: (1) It first links objects to vocabu- laries/taxonomies and resolves the ambiguity caused by objects that can be used for multiple purposes. (2) It next links objects to composite classes that merge classes in different kinds of vocabularies/taxonomies (e.g. classes in vocabularies for movie genres and those for directors) to avoid low prediction accuracy caused by rough-grained semantics. (3) It then lifts sparsely observed objects into their classes to solve the sparsity problem for rarely observed objects. To the best of our knowledge, this is the first study that leverages seman- tics to inject expert knowledge into tensor factorization. Experiments show that SRTF achieves up to 10% higher accuracy than state-of-the-art methods.
【Keywords】: Collaborative Filtering; Recommender Systems; Semantic Web; Machine Learning; Tensor Factorization; Linked Open Data; Link Prediction
【Paper Link】 【Pages】:2013-2019
【Authors】: Cam-Tu Nguyen ; Xiaoliang Wang ; Jing Liu ; Zhi-Hua Zhou
【Abstract】: Multi-Instance Multi-Label (MIML) is a learning framework where an example is associated with multiple labels and represented by a set of feature vectors (multiple instances). In the formalization of MIML learning, instances come from a single source (single view). To leverage multiple information sources (multi-view), we develop a multi-view MIML framework based on hierarchical Bayesian Network, and derive an effective learning algorithm based on variational inference. The model can naturally deal with examples in which some views could be absent (partial examples). On multi-view datasets, it is shown that our method is better than other multi-view and single-view approaches particularly in the presence of partial examples. On single-view benchmarks, extensive evaluation shows that our method is highly competitive or better than other MIML approaches on labeling examples and instances. Moreover, our method can effectively handle datasets with a large number of labels.
【Keywords】: multi-view, multi-instance, multi-label learning
【Paper Link】 【Pages】:2020-2026
【Authors】: Hidekazu Oiwa ; Hiroshi Nakagawa
【Abstract】: Sequential learning for classification tasks is an effective tool in the machine learning community. In sequential learning settings, algorithms sometimes make incorrect predictions on data that were correctly classified in the past. This paper explicitly deals with such inconsistent prediction behavior. Our main contributions are 1) to experimentally show its effect for user utilities as a human cognitive bias, 2) to formalize a new framework by internalizing this bias into the optimization problem, 3) to develop new algorithms without memorization of the past prediction history, and 4) to show some theoretical guarantees of our derived algorithm for both online and stochastic learning settings. Our experimental results show the superiority of the derived algorithm for problems involving human cognition.
【Keywords】: Online Learning; Stochastic Learning; Human Cognitive Bias; Stochastic Gradient Descent; Endowment Effect
【Paper Link】 【Pages】:2027-2033
【Authors】: Qihe Pan ; Deguang Kong ; Chris H. Q. Ding ; Bin Luo
【Abstract】: Dictionary learning plays an important role in machine learning, where data vectors are modeled as a sparse linear combinations of basis factors (i.e., dictionary). However, how to conduct dictionary learning in noisy environment has not been well studied. Moreover, in practice, the dictionary (i.e., the lower rank approximation of the data matrix) and the sparse representations are required to be nonnegative, such as applications for image annotation, document summarization, microarray analysis. In this paper, we propose a new formulation for non-negative dictionary learning in noisy environment, where structure sparsity is enforced on sparse representation. The proposed new formulation is also robust for data with noises and outliers, due to a robust loss function used. We derive an efficient multiplicative updating algorithm to solve the optimization problem, where dictionary and sparse representation are updated iteratively. We prove the convergence and correctness of proposed algorithm rigorously.We show the differences of dictionary at different level of sparsity constraint.The proposed algorithm can be adapted for clustering and semi-supervised learning.
【Keywords】: dictionary; NMF; robust
【Paper Link】 【Pages】:2034-2040
【Authors】: Fabio Panozzo ; Nicola Gatti ; Marcello Restelli
【Abstract】: Multi-agent learning is a challenging open task in artificial intelligence. It is known an interesting connection between multi-agent learning algorithms and evolutionary game theory, showing that the learning dynamics of some algorithms can be modeled as replicator dynamics with a mutation term. Inspired by the recent sequence-form replicator dynamics, we develop a new version of the Q-learning algorithm working on the sequence form of an extensive-form game allowing thus an exponential reduction of the dynamics length w.r.t. those of the normal form. The dynamics of the proposed algorithm can be modeled by using the sequence-form replicator dynamics with a mutation term. We show that, although sequence-form and normal-form replicator dynamics are realization equivalent, the Q-learning algorithm applied to the two forms have non-realization equivalent dynamics. Originally from the previous works on evolutionary game theory models form multi-agent learning, we produce an experimental evaluation to show the accuracy of the model.
【Keywords】: replicator dynamics; multi-agent learning; reinforcement learning; extensive-form games
【Paper Link】 【Pages】:2041-2047
【Authors】: Periklis A. Papakonstantinou ; Jia Xu ; Zhu Cao
【Abstract】: Bagging (Breiman 1996) and its variants is one of the most popular methods in aggregating classifiers and regressors. Originally, its analysis assumed that the bootstraps are built from an unlimited, independent source of samples, therefore we call this form of bagging ideal-bagging. However in the real world, base predictors are trained on data subsampled from a limited number of training samples and thus they behave very differently. We analyze the effect of intersections between bootstraps, obtained by subsampling, to train different base predictors. Most importantly, we provide an alternative subsampling method called design-bagging based on a new construction of combinatorial designs, and prove it universally better than bagging. Methodologically, we succeed at this level of generality because we compare the prediction accuracy of bagging and design-bagging relative to the accuracy ideal-bagging. This finds potential applications in more involved bagging-based methods. Our analytical results are backed up by experiments on classification and regression settings.
【Keywords】: bagging; bootstrapping; aggregation; combinatorial design
【Paper Link】 【Pages】:2048-2054
【Authors】: Maria Eugenia Ramirez-Loaiza ; Aron Culotta ; Mustafa Bilgic
【Abstract】: A common bottleneck in deploying supervised learning systems is collecting human-annotated examples. In many domains, annotators form an opinion about the label of an example incrementally -- e.g., each additional word read from a document or each additional minute spent inspecting a video helps inform the annotation. In this paper, we investigate whether we can train learning systems more efficiently by requesting an annotation before inspection is fully complete -- e.g., after reading only 25 words of a document. While doing so may reduce the overall annotation time, it also introduces the risk that the annotator might not be able to provide a label if interrupted too early. We propose an anytime active learning approach that optimizes the annotation time and response rate simultaneously. We conduct user studies on two document classification datasets and develop simulated annotators that mimic the users. Our simulated experiments show that anytime active learning outperforms several baselines on these two datasets. For example, with an annotation budget of one hour, training a classifier by annotating the first 25 words of each document reduces classification error by 17% over annotating the first 100 words of each document.
【Keywords】: active learning; non-uniform labeling cost; document classification
【Paper Link】 【Pages】:2055-2061
【Authors】: Lev Reyzin
【Abstract】: While boosting has been extensively studied, considerablyless attention has been devoted to the task of designing good weaklearning algorithms. In this paper we consider the problem of designing weak learners thatare especially adept to the boosting procedure and specifically the AdaBoost algorithm. First we describe conditions desirable for a weak learning algorithm. We then propose using sparse parity functions as weak learners, which have many of our desired properties, as weak learners in boosting. Our experimental tests show the proposed weak learners tobe competitive with the most widely used ones: decisionstumps and pruned decision trees.
【Keywords】: boosting; weak learners; parity functions
【Paper Link】 【Pages】:2062-2068
【Authors】: Paul Ruvolo ; Eric Eaton
【Abstract】: This paper develops an efficient online algorithm for learning multiple consecutive tasks based on the K-SVD algorithm for sparse dictionary optimization. We first derive a batch multi-task learning method that builds upon K-SVD, and then extend the batch algorithm to train models online in a lifelong learning setting. The resulting method has lower computational complexity than other current lifelong learning algorithms while maintaining nearly identical model performance. Additionally, the proposed method offers an alternate formulation for lifelong learning that supports both task and feature similarity matrices.
【Keywords】: lifelong learning; multi-task learning; sparse coding; dictionary optimization
【Paper Link】 【Pages】:2069-2077
【Authors】: Amir Sadeghipour ; Stefan Kopp
【Abstract】: In this paper, we present a hybrid grammar formalism designed to learn structured models of natural iconic gesture performances that allow for compressed representation and robust recognition. We analyze a dataset of iconic gestures and show how the proposed Feature-based Stochastic Context-Free Grammar (FSCFG) can generalize over both structural and feature-based variations among different gesture performances.
【Keywords】: Machine Learning; Classification; Gesture recognition; Feature-based Stochastic Context-free Grammar
【Paper Link】 【Pages】:2078-2084
【Authors】: Yuan Shi ; Aurélien Bellet ; Fei Sha
【Abstract】: We propose a new approach for metric learning by framing it as learning a sparse combination of locally discriminative metrics that are inexpensive to generate from the training data. This flexible framework allows us to naturally derive formulations for global, multi-task and local metric learning. The resulting algorithms have several advantages over existing methods in the literature: a much smaller number of parameters to be estimated and a principled way to generalize learned metrics to new testing data points. To analyze the approach theoretically, we derive a generalization bound that justifies the sparse combination. Empirically, we evaluate our algorithms on several datasets against state-of-the-art metric learning methods. The results are consistent with our theoretical findings and demonstrate the superiority of our approach in terms of classification performance and scalability.
【Keywords】: metric learning;sparsity
【Paper Link】 【Pages】:2085-2091
【Authors】: Le Shu ; Tianyang Ma ; Longin Jan Latecki
【Abstract】: In many practical cases, we need to generalize a model trained in a source domain to a new target domain.However, the distribution of these two domains may differ very significantly, especially sometimes some crucial target features may not have support in the source domain.This paper proposes a novel locality preserving projection method for domain adaptation task,which can find a linear mapping preserving the 'intrinsic structure' for both source and target domains.We first construct two graphs encoding the neighborhood information for source and target domains separately.We then find linear projection coefficients which have the property of locality preserving for each graph.Instead of combing the two objective terms under compatibility assumption and requiring the user to decide the importance of each objective function,we propose a multi-objective formulation for this problem and solve it simultaneously using Pareto optimization.The Pareto frontier captures all possible good linear projection coefficients that are preferred by one or more objectives.The effectiveness of our approach is justified by both theoretical analysis and empirical results on real world data sets.The new feature representation shows better prediction accuracy as our experiments demonstrate.
【Keywords】:
【Paper Link】 【Pages】:2092-2098
【Authors】: Nguyen Xuan Vinh ; Jeffrey Chan ; James Bailey
【Abstract】: Mutual information (MI) based approaches are a popular feature selection paradigm. Although the stated goal of MI-based feature selection is to identify a subset of features that share the highest mutual information with the class variable, most current MI-based techniques are greedy methods that make use of low dimensional MI quantities. The reason for using low dimensional approximation has been mostly attributed to the difficulty associated with estimating the high dimensional MI from limited samples. In this paper, we argue a different viewpoint that, given a very large amount of data, the high dimensional MI objective is still problematic to be employed as a meaningful optimization criterion, due to its overfitting nature: the MI almost always increases as more features are added, thus leading to a trivial solution which includes all features. We propose a novel approach to the MI-based feature selection problem, in which the overfitting phenomenon is controlled rigourously by means of a statistical test. We develop local and global optimization algorithms for this new feature selection model, and demonstrate its effectiveness in the applications of explaining variables and objects.
【Keywords】: feature selection; mutual information
【Paper Link】 【Pages】:2099-2105
【Authors】: Hao Wang ; Wei Wang ; Chen Zhang ; Fanjiang Xu
【Abstract】: Supervised metric learning plays a substantial role in statistical classification. Conventional metric learning algorithms have limited utility when the training data and testing data are drawn from related but different domains (i.e., source domain and target domain). Although this issue has got some progress in feature-based transfer learning, most of the work in this area suffers from non-trivial optimization and pays little attention to preserving the discriminating information. In this paper, we propose a novel metric learning algorithm to transfer knowledge from the source domain to the target domain in an information-theoretic setting, where a shared Mahalanobis distance across two domains is learnt by combining three goals together: 1) reducing the distribution difference between different domains; 2) preserving the geometry of target domain data; 3) aligning the geometry of source domain data with its label information. Based on this combination, the learnt Mahalanobis distance effectively transfers the discriminating power and propagates standard classifiers across these two domains. More importantly, our proposed method has closed-form solution and can be efficiently optimized. Experiments in two real-world applications demonstrate the effectiveness of our proposed method.
【Keywords】: metric learning; transfer learning; relative entropy
【Paper Link】 【Pages】:2106-2112
【Authors】: Qiaojun Wang ; Kai Zhang ; Guofei Jiang ; Ivan Marsic
【Abstract】: Semi-supervised kernel design is an essential step for obtaining good predictive performance in semi-supervised learning tasks. In the current literatures, a large family of algorithms builds the new kernel by using the weighted average of predefined base kernels. While optimal weighting schemes have been studied extensively, the choice of base kernels received much less attention. Many methods simply adopt the empirical kernel matrices or its eigenvectors. Such base kernels are computed irrespective of class labels and may not always reflect useful structures in the data. As a result, in case of poor base kernels, the generalization performance can be degraded however hard their weights are tuned. In this paper, we propose to construct high-quality base kernels with the help of label information to globally improve the final target alignment. In particular, we devise label-aware kernel eigenvectors under the framework of semi-supervised eigenfunction extrapolation, which span base kernels that are more useful for learning. Such base kernels are individually better aligned to the learning target, so their mixture will more likely generate a good classifier. Our approach is computationally efficient, and demonstrates encouraging performance in semi-supervised classification and regression.
【Keywords】:
【Paper Link】 【Pages】:2113-2120
【Authors】: Shusen Wang ; Bojun Tu ; Congfu Xu ; Zhihua Zhang
【Abstract】: Subspace clustering is an important unsupervised learning problem with wide applications in computer vision and data analysis. However, the state-of-the-art methods for this problem suffer from high time complexity---quadratic or cubic in $n$ (the number of data instances). In this paper we exploit a data selection algorithm to speedup computation and the robust principal component analysis to strengthen robustness. Accordingly, we devise a scalable and robust subspace clustering method which costs time only linear in $n$. We prove theoretically that under certain mild assumptions our method solves the subspace clustering problem exactly even for grossly corrupted data. Our algorithm is based on very simple ideas, yet it is the only linear time algorithm with noiseless or noisy recovery guarantee. Finally, empirical results verify our theoretical analysis.
【Keywords】: subspace clustering
【Paper Link】 【Pages】:2121-2127
【Authors】: Shusen Wang ; Chao Zhang ; Hui Qian ; Zhihua Zhang
【Abstract】: Determinantal point process (DPP) is an important probabilistic model that has extensive applications in artificial intelligence. The exact sampling algorithm of DPP requires the full eigenvalue decomposition of the kernel matrix which has high time and space complexities. This prohibits the applications of DPP from large-scale datasets. Previous work has applied the Nystrom method to speedup the sampling algorithm of DPP, and error bounds have been established for the approximation. In this paper we employ the matrix ridge approximation (MRA) to speedup the sampling algorithm of DPP, showing that our approach MRA-DPP has stronger error bound than the Nystrom-DPP. In certain circumstances our MRA-DPP is provably exact, whereas the Nystrom-DPP is far from the ground truth. Finally, experiments on several real-world datasets show that our MRA-DPP is more accurate than the other approximation approaches.
【Keywords】: kernel method; DPP; kernel approximation; the Nystrom method
【Paper Link】 【Pages】:2128-2134
【Authors】: Weiran Wang ; Miguel Á. Carreira-Perpiñán
【Abstract】: Dimensionality reduction (DR) is often used as a preprocessing step in classification, but usually one first fixes the DR mapping, possibly using label information, and then learns a classifier (a filter approach). Best performance would be obtained by optimizing the classification error jointly over DR mapping and classifier (a wrapper approach), but this is a difficult nonconvex problem, particularly with nonlinear DR. Using the method of auxiliary coordinates, we give a simple, efficient algorithm to train a combination of nonlinear DR and a classifier, and apply it to a RBF mapping with a linear SVM. This alternates steps where we train the RBF mapping and a linear SVM as usual regression and classification, respectively, with a closed-form step that coordinates both. The resulting nonlinear low-dimensional classifier achieves classification errors competitive with the state-of-the-art but is fast at training and testing, and allows the user to trade off runtime for classification accuracy easily. We then study the role of nonlinear DR in linear classification, and the interplay between the DR mapping, the number of latent dimensions and the number of classes. When trained jointly, the DR mapping takes an extreme role in eliminating variation: it tends to collapse classes in latent space, erasing all manifold structure, and lay out class centroids so they are linearly separable with maximum margin.
【Keywords】: nonconvex optimization, classification, wrapper approaches
【Paper Link】 【Pages】:2135-2141
【Authors】: Yining Wang ; Jun Zhu
【Abstract】: Infinite SVM (iSVM) is a Dirichlet process (DP) mixture of large-margin classifiers. Though flexible in learning nonlinear classifiers and discovering latent clustering structures, iSVM has a difficult inference task and existing methods could hinder its applicability to large-scale problems. This paper presents a small-variance asymptotic analysis to derive a simple and efficient algorithm, which monotonically optimizes a max-margin DP-means (M2DPM) problem, an extension of DP-means for both predictive learning and descriptive clustering. Our analysis is built on Gibbs infinite SVMs, an alternative DP mixture of large-margin machines, which admits a partially collapsed Gibbs sampler without truncation by exploring data augmentation techniques. Experimental results show that M2DPM runs much faster than similar algorithms without sacrificing prediction accuracies.
【Keywords】: Infinite SVM; Gibbs sampling; small variance asymptotics; max-margin DP-means; data augmentation
【Paper Link】 【Pages】:2142-2148
【Authors】: Pengcheng Wu ; Yi Ding ; Peilin Zhao ; Chunyan Miao ; Steven C. H. Hoi
【Abstract】: Learning relative similarity from pairwise instances is an important problem in machine learning and has a wide range of applications. Despite being studied for years, some existing methods solved by Stochastic Gradient Descent (SGD) techniques generally suffer from slow convergence. In this paper, we investigate the application of Stochastic Dual Coordinate Ascent (SDCA) technique to tackle the optimization task of relative similarity learning by extending from vector to matrix parameters. Theoretically, we prove the optimal linear convergence rate for the proposed SDCA algorithm, beating the well-known sublinear convergence rate by the previous best metric learning algorithms. Empirically, we conduct extensive experiments on both standard and large-scale data sets to validate the effectiveness of the proposed algorithm for retrieval tasks.
【Keywords】: distance metric learning; similarity learning; online learning; retrieval
【Paper Link】 【Pages】:2149-2155
【Authors】: Rongkai Xia ; Yan Pan ; Lei Du ; Jian Yin
【Abstract】: Multi-view clustering, which seeks a partition of the data inmultiple views that often provide complementary information to eachother, has received considerable attention in recent years. In reallife clustering problems, the data in each view may haveconsiderable noise. However, existing clustering methods blindlycombine the information from multi-view data with possiblyconsiderable noise, which often degrades their performance. In thispaper, we propose a novel Markov chain method for RobustMulti-view Spectral Clustering (RMSC). Our method has a flavor oflow-rank and sparse decomposition, where we firstly construct atransition probability matrix from each single view, and then usethese matrices to recover a shared low-rank transition probabilitymatrix as a crucial input to the standard Markov chain methodfor clustering. The optimization problem of RMSC has a low-rankconstraint on the transition probability matrix, and simultaneouslya probabilistic simplex constraint on each of its rows. To solvethis challenging optimization problem, we propose an optimization procedurebased on the Augmented Lagrangian Multiplier scheme. Experimentalresults on various real world datasets show that theproposed method has superior performance over severalstate-of-the-art methods for multi-view clustering.
【Keywords】: multi-view clustering; low-rank matrices; Markov chains
【Paper Link】 【Pages】:2156-2162
【Authors】: Rongkai Xia ; Yan Pan ; Hanjiang Lai ; Cong Liu ; Shuicheng Yan
【Abstract】: Hashing is a popular approximate nearest neighbor search approach for large-scale image retrieval. Supervised hashing, which incorporates similarity/dissimilarity information on entity pairs to improve the quality of hashing function learning, has recently received increasing attention. However, in the existing supervised hashing methods for images, an input image is usually encoded by a vector of hand-crafted visual features. Such hand-crafted feature vectors do not necessarily preserve the accurate semantic similarities of images pairs, which may often degrade the performance of hashing function learning. In this paper, we propose a supervised hashing method for image retrieval, in which we automatically learn a good image representation tailored to hashing as well as a set of hash functions. The proposed method has two stages. In the first stage, given the pairwise similarity matrix $S$ over training images, we propose a scalable coordinate descent method to decompose $S$ into a product of $HH^T$ where $H$ is a matrix with each of its rows being the approximate hash code associated to a training image. In the second stage, we propose to simultaneously learn a good feature representation for the input images as well as a set of hash functions, via a deep convolutional network tailored to the learned hash codes in $H$ and optionally the discrete class labels of the images. Extensive empirical evaluations on three benchmark datasets with different kinds of images show that the proposed method has superior performance gains over several state-of-the-art supervised and unsupervised hashing methods.
【Keywords】: supervised hashing; convolutional neural networks; image retrieval
【Paper Link】 【Pages】:2163-2169
【Authors】: Bo Xin ; Yoshinobu Kawahara ; Yizhou Wang ; Wen Gao
【Abstract】: Generalized fused lasso (GFL) penalizes variables with L1 norms based both on the variables and their pairwise differences. GFL is useful when applied to data where prior information is expressed using a graph over the variables. However, the existing GFL algorithms incur high computational costs and they do not scale to high-dimensional problems. In this study, we propose a fast and scalable algorithm for GFL. Based on the fact that fusion penalty is the Lov'asz extension of a cut function, we show that the key building block of the optimization is equivalent to recursively solving parametric graph-cut problems. Thus, we use a parametric flow algorithm to solve GFL in an efficient manner. Runtime comparisons demonstrated a significant speed-up compared with the existing GFL algorithms. By exploiting the scalability of the proposed algorithm, we formulated the diagnosis of Alzheimer's disease as GFL. Our experimental evaluations demonstrated that the diagnosis performance was promising and that the selected critical voxels were well structured i.e., connected, consistent according to cross-validation and in agreement with prior clinical knowledge.
【Keywords】: generalized fused lasso; Alzhemier's disease; parametric graph-cut
【Paper Link】 【Pages】:2170-2176
【Authors】: Tianbing Xu ; Jianfeng Gao ; Lin Xiao ; Amelia C. Regan
【Abstract】: We propose a voted dual averaging method for on- line classification problems with explicit regularization. This method employs the update rule of the regularized dual averaging (RDA) method proposed by Xiao, but only on the subsequence of training examples where a classification error is made. We derive a bound on the number of mistakes made by this method on the training set, as well as its generalization error rate. We also intro- duce the concept of relative strength of regularization, and show how it affects the mistake bound and gener- alization performance. We examine the method using l1-regularization on a large-scale natural language pro- cessing task, and obtained state-of-the-art classification performance with fairly sparse models.
【Keywords】: voted RDA, online classification, parse reranking
【Paper Link】 【Pages】:2177-2183
【Authors】: Dongqing Zhang ; Wu-Jun Li
【Abstract】: Due to its low storage cost and fast query speed, hashing has been widely adopted for similarity search in multimedia data. In particular, more and more attentions have been payed to multimodal hashing for search in multimedia data with multiple modalities, such as images with tags. Typically, supervised information of semantic labels is also available for the data points in many real applications. Hence, many supervised multimodal hashing~(SMH) methods have been proposed to utilize such semantic labels to further improve the search accuracy. However, the training time complexity of most existing SMH methods is too high, which makes them unscalable to large-scale datasets. In this paper, a novel SMH method, called semantic correlation maximization~(SCM), is proposed to seamlessly integrate semantic labels into the hashing learning procedure for large-scale data modeling. Experimental results on two real-world datasets show that SCM can significantly outperform the state-of-the-art SMH methods, in terms of both accuracy and scalability.
【Keywords】:
【Paper Link】 【Pages】:2184-2190
【Authors】: Wei-Jia Zhang ; Zhi-Hua Zhou
【Abstract】: Multi-instance learning deals with tasks where each example is a bag of instances, and the bag labels of training data are known whereas instance labels are unknown. Most previous studies on multi-instance learning assumed that the training and testing data are from the same distribution; however, this assumption is often violated in real tasks. In this paper, we present possibly the first study on multi-instance learning with distribution change. We propose the MICS approach by considering both bag-level and instance-level distribution change. Experiments show that MICS is almost always significantly better than many state-of-the-art multi-instance learning algorithms when distribution change occurs; and even when there is no distribution change, their performances are still comparable.
【Keywords】:
【Paper Link】 【Pages】:2191-2197
【Authors】: Xianchao Zhang ; Han Liu ; Xiaotong Zhang ; Xinyue Liu
【Abstract】: Density-based techniques seem promising for handling datauncertainty in uncertain data clustering. Nevertheless, someissues have not been addressed well in existing algorithms. Inthis paper, we firstly propose a novel density-based uncertaindata clustering algorithm, which improves upon existing algorithmsfrom the following two aspects: (1) it employs anexact method to compute the probability that the distance betweentwo uncertain objects is less than or equal to a boundaryvalue, instead of the sampling-based method in previouswork; (2) it introduces new definitions of core object probabilityand direct reachability probability, thus reducing thecomplexity and avoiding sampling. We then further improvethe algorithm by using a novel assignment strategy to ensurethat every object will be assigned to the most appropriatecluster. Experimental results show the superiority of our proposedalgorithms over existing ones.
【Keywords】: clustering; uncertain data;
【Paper Link】 【Pages】:2198-2205
【Authors】: Jiangchuan Zheng ; Siyuan Liu ; Lionel M. Ni
【Abstract】: Inverse reinforcement learning (IRL) aims to recover the reward function underlying a Markov Decision Process from behaviors of experts in support of decision-making. Most recent work on IRL assumes the same level of trustworthiness of all expert behaviors, and frames IRL as a process of seeking reward function that makes those behaviors appear (near)-optimal. However, it is common in reality that noisy expert behaviors disobeying the optimal policy exist, which may degrade the IRL performance significantly. To address this issue, in this paper, we develop a robust IRL framework that can accurately estimate the reward function in the presence of behavior noise. In particular, we focus on a special type of behavior noise referred to as sparse noise due to its wide popularity in real-world behavior data. To model such noise, we introduce a novel latent variable characterizing the reliability of each expert action and use Laplace distribution as its prior. We then devise an EM algorithm with a novel variational inference procedure in the E-step, which can automatically identify and remove behavior noise in reward learning. Experiments on both synthetic data and real vehicle routing data with noticeable behavior noise show significant improvement of our method over previous approaches in learning accuracy, and also show its power in de-noising behavior data.
【Keywords】: Inverse Reinforcement Learning; Robust Model; Sparse Behavior Noise; Variational Inference
【Paper Link】 【Pages】:2206-2212
【Authors】: Wenliang Zhong ; James T. Kwok
【Abstract】: Sparse modeling has been highly successful in many real-world applications. While a lot of interests have been on convex regularization, recent studies show that nonconvexregularizers can outperform their convex counterparts in many situations.However, the resulting nonconvex optimization problems are often challenging, especiallyfor composite regularizers such as the nonconvex overlapping group lasso. In thispaper, byusing a recent mathematical tool known as the proximal average,we propose a novel proximal gradient descent method for optimization with a wide class of nonconvex and composite regularizers.Instead of directlysolving the proximal stepassociated with a composite regularizer, we average thesolutions from the proximal problems of the constituent regularizers. This simple strategy has guaranteed convergenceand low per-iteration complexity.Experimental results on a number of synthetic andreal-world data sets demonstrate the effectiveness and efficiency of theproposed optimization algorithm, and also the improved classification performanceresulting from thenonconvex regularizers.
【Keywords】: nonconvex optimization, composite regularization
【Paper Link】 【Pages】:2213-2220
【Authors】: Joey Tianyi Zhou ; Sinno Jialin Pan ; Ivor W. Tsang ; Yan Yan
【Abstract】: Most previous heterogeneous transfer learning methods learn a cross-domain feature mapping between heterogeneous feature spaces based on a few cross-domain instance-correspondences, and these corresponding instances are assumed to be representative in the source and target domains respectively. However, in many real-world scenarios, this assumption may not hold. As a result, the constructed feature mapping may not be precisely due to the bias issue of the correspondences in the target or (and) source domain(s). In this case, a classifier trained on the labeled transformed-source-domain data may not be useful for the target domain. In this paper, we present a new transfer learning framework called Hybrid Heterogeneous Transfer Learning (HHTL), which allows the corresponding instances across domains to be biased in either the source or target domain. Specifically, we propose a deep learning approach to learn a feature mapping between cross-domain heterogeneous features as well as a better feature representation for mapped data to reduce the bias issue caused by the cross-domain correspondences. Extensive experiments on several multilingual sentiment classification tasks verify the effectiveness of our proposed approach compared with some baseline methods.
【Keywords】:
【Paper Link】 【Pages】:2221-2227
【Authors】: Meysam Aghighi ; Peter Jonsson
【Abstract】: Many real-world planning problems are oversubscription problems where all goals are not simultaneously achievable and the planner needs to find a feasible subset. We present complexity results for the so-called partial satisfaction and net benefit problems under various restrictions; this extends previous work by van den Briel et al. Our results reveal strong connections between these problems and with classical planning. We also present a method for efficiently compiling oversubscription problems into the ordinary plan existence problem; this can be viewed as a continuation of earlier work by Keyder & Geffner.
【Keywords】: oversubscription planning; computational complexity; compilability
【Paper Link】 【Pages】:2228-2234
【Authors】: Sergiy Bogomolov ; Daniele Magazzeni ; Andreas Podelski ; Martin Wehrle
【Abstract】: Planning in hybrid domains is an important and challenging task, and various planning algorithms have been proposed in the last years. From an abstract point of view, hybrid planning domains are based on hybrid automata, which have been studied intensively in the model checking community. In particular, powerful model checking algorithms and tools have emerged for this formalism. However, despite the quest for more scalable planning approaches, model checking algorithms have not been applied to planning in hybrid domains so far. In this paper, we make a first step in bridging the gap between these two worlds. We provide a formal translation scheme from PDDL+ to the standard formalism of hybrid automata, as a solid basis for using hybrid system model-checking tools for dealing with hybrid planning domains. As a case study, we use the SpaceEx model checker, showing how we can address PDDL+ domains that are out of the scope of state-of-the-art planners.
【Keywords】: planning; model checking; hybrid systems; PDDL+
【Paper Link】 【Pages】:2235-2241
【Authors】: Blai Bonet ; Hector Geffner
【Abstract】: The problem of on-line planning in partially observable settings involves two problems: keeping track of beliefs about the environment and selecting actions for achieving goals. While the two problems are computationally intractable in the worst case, significant progress has been achieved in recent years through the use of suitable reductions. In particular, the state-of-the-art CLG planner is based on a translation that maps deterministic partially observable problems into fully observable non-deterministic ones. The translation, which is quadratic in the number of problem fluents and gets rid of the belief tracking problem, is adequate for most benchmarks, and it is in fact complete for problems that have width 1. The more recent K-replanner uses translations that are linear, one for keeping track of beliefs and the other for selecting actions using off-the-shelf classical planners. As a result, the K-replanner scales up better but it is not as general. In this work, we combine the benefits of the two approaches - the scope of the CLG planner and the efficiency of the Kreplanner. The new planner, called LW1, is based on a translation that is linear but complete for width-1 problems. The scope and scalability of the new planner is evaluated experimentally by considering the existing benchmarks and new problems.
【Keywords】: Contingent Planning; POMDP; Belief Tracking; Action Selection; Completeness; Width;
【Paper Link】 【Pages】:2242-2249
【Authors】: Alessandro Cimatti ; Luke Hunsberger ; Andrea Micheli ; Marco Roveri
【Abstract】: A Simple Temporal Network with Uncertainty (STNU) is a structure for representing and reasoning about temporal constraints in domains where some temporal durations are not controlled by the executor. The most important property of an STNU is whether it is dynamically controllable (DC) whether there exists a strategy for executing the controllable time-points that guarantees that all constraints will be satisfied no matter how the uncontrollable durations turn out. This paper provides a novel mapping from STNUs to Timed Game Automata (TGAs) that: (1) explicates the deep theoretical relationships between STNUs and TGAs; and (2) enables the memoryless strategies generated from the TGA to be transformed into equivalent STNU execution strategies that reduce the real-time computational burden for the executor. The paper formally proves that the STNU-to-TGA encoding properly captures the execution semantics of STNUs.
【Keywords】: Temporal reasoning; Simple Temporal Problems with Uncertainty; Dynamic Controllability; Timed Game Automata; Strategy Synthesis
【Paper Link】 【Pages】:2250-2256
【Authors】: Brian Coltin ; Manuela M. Veloso
【Abstract】: In pickup and delivery problems (PDPs), vehicles pickup and deliver a set of items under various constraints. We address the PDP with Transfers (PDP-T), in which vehicles plan to transfer items between one another to form more efficient schedules. We introduce the Very Large Neighborhood Search with Transfers (VLNS-T) algorithm to form schedules for the PDP-T. Our approach allows multiple transfers for items at arbitrary locations, and is not restricted to a set of predefined transfer points. We show that VLNS-T improves upon the best known PDP solutions for benchmark problems, and demonstrate its effectiveness on problems sampled from real world taxi data in New York City.
【Keywords】: scheduling; pickup and delivery problems; transfers
【Paper Link】 【Pages】:2257-2263
【Authors】: Nicolas Drougard ; Florent Teichteil-Königsbuch ; Jean-Loup Farges ; Didier Dubois
【Abstract】: Qualitative Possibilistic Mixed-Observable MDPs (pi-MOMDPs), generalizing pi-MDPs and pi-POMDPs, are well-suited models to planning under uncertainty with mixed-observability when transition, observation and reward functions are not precisely known and can be qualitatively described. Functions defining the model as well as intermediate calculations are valued in a finite possibilistic scale L, which induces a finite belief state space under partial observability contrary to its probabilistic counterpart. In this paper, we propose the first study of factored pi-MOMDP models in order to solve large structured planning problems under qualitative uncertainty, or considered as qualitative approximations of probabilistic problems. Building upon the SPUDD algorithm for solving factored (probabilistic) MDPs, we conceived a symbolic algorithm named PPUDD for solving factored pi-MOMDPs. Whereas SPUDD's decision diagrams' leaves may be as large as the state space since their values are real numbers aggregated through additions and multiplications, PPUDD's ones always remain in the finite scale L via min and max operations only. Our experiments show that PPUDD's computation time is much lower than SPUDD, Symbolic-HSVI and APPL for possibilistic and probabilistic versions of the same benchmarks under either total or mixed observability, while still providing high-quality policies.
【Keywords】: Qualitative Planning under Uncertainty; Symbolic Planning with Decision Diagrams; Possibilistic POMDPs; Mixed Observability
【Paper Link】 【Pages】:2264-2270
【Authors】: Cheng Fang ; Peng Yu ; Brian C. Williams
【Abstract】: Scheduling under uncertainty is essential to many autonomous systems and logistics tasks. Probabilistic methods for solving temporal problems exist which quantify and attempt to minimize the probability of schedule failure. These methods are overly conservative, resulting in a loss in schedule utility. Chance constrained formalism address over-conservatism by imposing bounds on risk, while maximizing utility subject to these risk bounds. In this paper we present the probabilistic Simple Temporal Network (pSTN), a probabilistic formalism for representing temporal problems with bounded risk and a utility over event timing. We introduce a constrained optimisation algorithm for pSTNs that achieves compactness and efficiency through a problem encoding in terms of a parameterised STNU and its reformulation as a parameterised STN. We demonstrate through a car sharing application that our chance-constrained approach runs in the same time as the previous probabilistic approach, yields solutions with utility improvements of at least 5% over previous arts, while guaranteeing operation within the specified risk bound.
【Keywords】: chance-constrained; probabilistic
【Paper Link】 【Pages】:2271-2277
【Authors】: Marc Goerigk ; Richard Hoshino ; Ken-ichi Kawarabayashi ; Stephan Westphal
【Abstract】: The Traveling Tournament Problem (TTP) is a complex problem in sports scheduling whose solution is a schedule of home and away games meeting specific feasibility requirements, while minimizing the total distance traveled by all the teams. A recently-developed "hybrid" algorithm, combining local search and integer programming, has resulted in best-known solutions for many TTP instances. In this paper, we tackle the TTP from a graph-theoretic perspective, by generating a new "canonical" schedule in which each team's three-game road trips match up with the underlying graph's minimum-weight P_3-packing. By using this new schedule as the initial input for the hybrid algorithm, we develop tournament schedules for five benchmark TTP instances that beat all previously-known solutions.
【Keywords】: traveling tournament problem; heuristic search; graph theory; scheduling theory
【Paper Link】 【Pages】:2278-2284
【Authors】: S. Ali Hojjat ; John Turner ; Suleyman Cetintas ; Jian Yang
【Abstract】: We propose a novel idea in the allocation and serving of online advertising. We show that by using predetermined fixed-length streams of ads (which we call patterns) to serve advertising, we can incorporate a variety of interesting features into the ad allocation optimization problem. In particular, our formulation optimizes for representativeness as well as user-level diversity and pacing of ads, under reach and frequency requirements. We show how the problem can be solved efficiently using a column generation scheme in which only a small set of best patterns are kept in the optimization problem. Our numerical tests suggest that with parallelization of the pattern generation process, the algorithm has a promising run time and memory usage.
【Keywords】: column generation; patterns; reach; frequency; advertising
【Paper Link】 【Pages】:2285-2292
【Authors】: Ping Hou ; William Yeoh ; Tran Cao Son
【Abstract】: While MDPs are powerful tools for modeling sequential decision making problems under uncertainty, they are sensitive to the accuracy of their parameters. MDPs with uncertainty in their parameters are called Uncertain MDPs. In this paper, we introduce a general framework that allows off-the-shelf MDP algorithms to solve Uncertain MDPs by planning based on currently available information and replan if and when the problem changes. We demonstrate the generality of this approach by showing that it can use the VI, TVI, ILAO*, LRTDP, and UCT algorithms to solve Uncertain MDPs. We experimentally show that our approach is typically faster than replanning from scratch and we also provide a way to estimate the amount of speedup based on the amount of information being reused.
【Keywords】:
【Paper Link】 【Pages】:2293-2299
【Authors】: Uwe Köckemann ; Federico Pecora ; Lars Karlsson
【Abstract】: Consider a family whose home is equipped with several service robots. The actions planned for the robots must adhere to Interaction Constraints (ICs) relating them to human activities and preferences. These constraints must be sufficiently expressive to model both temporal and logical dependencies among robot actions and human behavior, and must accommodate incomplete information regarding human activities. In this paper we introduce an approach for automatically generating plans that are conformant wrt. given ICs and partially specified human activities. The approach allows to separate causal reasoning about actions from reasoning about ICs, and we illustrate the computational advantage this brings with experiments on a large-scale (semi-)realistic household domain with hundreds of human activities and several robots.
【Keywords】: Constraint-based Planning; Human-aware Planning; Planning in Inhabited Environments
【Paper Link】 【Pages】:2300-2307
【Authors】: Martin Kronegger ; Sebastian Ordyniak ; Andreas Pfandler
【Abstract】: Backdoors measure the distance to tractable fragments and have become an important tool to find fixed-parameter tractable (fpt) algorithms. Despite their success, backdoors have not been used for planning, a central problem in AI that has a high computational complexity. In this work, we introduce two notions of backdoors building upon the causal graph. We analyze the complexity of finding a small backdoor (detection) and using the backdoor to solve the problem (evaluation) in the light of planning with (un)bounded plan length/domain of the variables. For each setting we present either an fpt-result or rule out the existence thereof by showing parameterized intractability. In three cases we achieve the most desirable outcome: detection and evaluation are fpt.
【Keywords】: planning;backdoors;theoretical analysis; parameterized complexity;causal graph
【Paper Link】 【Pages】:2308-2314
【Authors】: T. K. Satish Kumar ; Duc Thien Nguyen ; William Yeoh ; Sven Koenig
【Abstract】: In this paper, we describe a simple randomized algorithm that runs in polynomial time and solves connected row convex (CRC) constraints in distributed settings. CRC constraints generalize many known tractable classes of constraints like 2-SAT and implicational constraints. They can model problems in many domains including temporal reasoning and geometric reasoning, and generally speaking, play the role of ``Gaussians'' in the logical world. Our simple randomized algorithm for solving them in distributed settings, therefore, has a number of important applications. We support our claims through a theoretical analysis and empirical results.
【Keywords】:
【Paper Link】 【Pages】:2315-2321
【Authors】: Johannes Löhr ; Martin Wehrle ; Maria Fox ; Bernhard Nebel
【Abstract】: Planning-based methods to guide switched hybrid systems from an initial state into a desired goal region opens an interesting field for control. The idea of the Domain Predictive Control (DPC) approach is to generate input signals affecting both the numerical states and the modes of the system by stringing together atomic actions to a logically consistent plan. However, the existing DPC approach is restricted in the sense that a discrete and pre-defined input signal is required for each action. In this paper, we extend the approach to deal with symbolic states. This allows for the propagation of reachable regions of the state space emerging from actions with inputs that can be arbitrarily chosen within specified input bounds. This symbolic extension enables the applicability of DPC to systems with bounded inputs sets and increases its robustness due to the implicitly reduced search space. Moreover, precise numeric goal states instead of goal regions become reachable.
【Keywords】: domain predictive control; hybrid domains; dynamic systems;
【Paper Link】 【Pages】:2322-2329
【Authors】: Christian J. Muise ; Vaishak Belle ; Sheila A. McIlraith
【Abstract】: Planning with sensing actions under partial observability is a computationally challenging problem that is fundamental to the realization of AI tasks in areas as diverse as robotics, game playing, and diagnostic problem solving. Recent work on generating plans for partially observable domains has advocated for online planning, claiming that offline plans are often too large to generate. Here we push the envelope on this challenging problem, proposing a technique for generating conditional (aka contingent) plans offline. The key to our planner's success is the reliance on state-of-the-art techniques for fully observable non-deterministic (FOND) planning. In particular, we use an existing compilation for converting a planning problem under partial observability and sensing to a FOND planning problem. With a modified FOND planner in hand, we are able to scale beyond previous techniques for generating conditional plans with solutions that are orders of magnitude smaller than previously possible in some domains.
【Keywords】: contingent planning; non-deterministic planning; strong cyclic planning; FOND; conditional effects; state relevance
【Paper Link】 【Pages】:2330-2336
【Authors】: James G. Paterson ; Eric Timmons ; Brian Charles Williams
【Abstract】: A wide range of robotic missions contain actions that exhibit looping behavior. Examples of these actions include picking fruit in agriculture, pick-and-place tasks in manufacturing and search patterns in robotic search or survey missions. These looping actions often have a range of acceptable values for the number of loops and a preference function over them. For example, during robotic survey missions, the information gain is expected to increase with the number of loops in a search pattern. Since these looping actions also take time, which is typically bounded, there is a challenge of maximizing utility while respecting time constraints. In this paper, we introduce the Looping Temporal Problem with Preference (LTPP) as a simple parameterized extension of a simple temporal problem. In addition, we introduce a scheduling algorithm for LTPPs which leverages the structure of the problem to find the optimal solution efficiently. We show more than an order of magnitude improvement in run-time over current scheduling techniques and framing a LTPP as a MINLP.
【Keywords】: Schedule; Loops; Preference; Optimization;
【Paper Link】 【Pages】:2337-2343
【Authors】: Eleanor G. Rieffel ; Davide Venturelli ; Minh Do ; Itay Hen ; Jeremy Frank
【Abstract】: There are two complementary ways to evaluate planning algorithms: performance on benchmark problems derived from real applications and analysis of performance on parametrized families of problems with known properties. Prior to this work, few means of generating parametrized families of hard planning problems were known. We generate hard planning problems from the solvable/unsolvable phase transition region of well-studied NP-complete problems that map naturally to navigation and scheduling, aspects common to many planning domains. We observe significant differences between state-of-the-art planners on these problem families, enabling us to gain insight into the relative strengths and weaknesses of these planners. Our results confirm exponential scaling of hardness with problem size, even at very small problem sizes. These families provide complementary test sets exhibiting properties not found in existing benchmarks.
【Keywords】: automated planning; phase transitions; benchmarks; benchmark sets; planning algorithms; scheduling; parametrized problem families
【Paper Link】 【Pages】:2344-2351
【Authors】: Nathan Robinson ; Sheila A. McIlraith ; David Toman
【Abstract】: In this paper we revisit the problem of generating query plans using AI automated planning with a view to leveraging significant recent advances in state-of-the-art planning techniques. Our efforts focus on the specific problem of cost-based join-order optimization for conjunctive relational queries, a critical component of production-quality query optimizers. We characterize the general query-planning problem as a delete-free planning problem, and query plan optimization as a context-sensitive cost-optimal planning problem. We propose algorithms that generate high-quality query plans, guaranteeing optimality under certain conditions. Our approach is general, supporting the use of a broad suite of domain-independent and domain-specific optimization criteria. Experimental results demonstrate the effectiveness of AI planning techniques for query plan generation and optimization.
【Keywords】: Planning; Query optimization; Join-order Selection
【Paper Link】 【Pages】:2352-2357
【Authors】: Malcolm Ryan
【Abstract】: In this paper we investigate three different approaches to encoding domain-dependent control knowledge for Answer-Set Planning. Starting with a standard imple- mentation of the action description language B, we add control knowledge expressed in the GOLOG logic pro- gramming language. A naive encoding, following the original definitions of Levesque et al., is shown to scale poorly. We examine two alternative codings based on the transition semantics of ConGOLOG. We show that a speed increase of multiple orders of magnitude can be obtain by compiling the GOLOG program into a finite- state machine representation.
【Keywords】: GOLOG; answer set programming; planning
【Paper Link】 【Pages】:2358-2366
【Authors】: Silvan Sievers ; Martin Wehrle ; Malte Helmert
【Abstract】: Label reduction is a technique for simplifying families of labeled transition systems by dropping distinctions between certain transition labels. While label reduction is critical to the efficient computation of merge-and-shrink heuristics, current theory only permits reducing labels in a limited number of cases. We generalize this theory so that labels can be reduced in every intermediate abstraction of a merge-and-shrink tree. This is particularly important for efficiently computing merge-and-shrink abstractions based on non-linear merge strategies. As a case study, we implement a non-linear merge strategy based on the original work on merge-and-shrink heuristics in model checking by Dräger et al.
【Keywords】: abstraction heuristics; merge-and-shrink; label reduction
【Paper Link】 【Pages】:2367-2373
【Authors】: Jonathan Sprauel ; Andrey Kolobov ; Florent Teichteil-Königsbuch
【Abstract】: In many probabilistic planning scenarios, a system’s behavior needs to not only maximize the expected utility but also obey certain restrictions. This paper presents Saturated Path-Constrained Markov Decision Processes (SPC MDPs), a new MDP type for planning under uncertainty with deterministic model-checking constraints, e.g., "state s must be visited befores s'", "the system must end up in s", or "the system must never enter s". We present a mathematical analysis of SPCMDPs, showing that although SPC MDPs generally have no optimal policies, every instance of this class has an epsilon-optimal randomized policy for any > 0. We propose a dynamic programming-based algorithm for finding such policies, and empirically demonstrate this algorithm to be orders of magnitude faster than its next-best alternative.
【Keywords】: Safe and Optimal Controller Synthesis; Uncertainty and Stochasticity; Planning under Uncertainty; Model-Checking PCTL Constraints; Path-Constrained Markov Decision Processes
【Paper Link】 【Pages】:2374-2380
【Authors】: Ran Taig ; Ronen I. Brafman
【Abstract】: Conformant probabilistic planning (CPP) differs from conformant planning (CP) by two key elements: the initial belief state is probabilistic,and the conformant plan must achieve the goal with probability $\geq\theta$, for some $0<\theta\leq 1$. In earlier work we observed that one can reduce CPP to CP by finding a set of initial states whose probability $\geq\theta$, for whicha conformant plan exists. In previous solvers we used the underlying planner to select this set of states and to plan for them simultaneously. Here we suggest an alternative approach: start with relevance analysis to determine a promising set of initial states on which to focus. Then, call an off-the-shelf conformant planner to solve the resulting problem. This approach has a number of advantages. First, instead of depending on the heuristic function to select the set of initial states,we can introduce specific, efficient relevance reasoning techniques. Second, we can benefit from optimizations used by conformant planners that are unsound when applied to the original CPP. Finally, we are free to use any existing (or new) CP solver. Consequently, the new planner dominates previous solvers on almost all domains and scales to instances that were not solved before.
【Keywords】: compilation based approach, conformant probabilistic planning
【Paper Link】 【Pages】:2381-2387
【Authors】: Cees Witteveen ; Michel Wilson ; Tomas Klos
【Abstract】: Decomposition is a technique to obtain complete solutions by assembling independently obtained partial solutions. In particular, constraint decomposition plays an important role in distributed databases, distributed scheduling and violation detection: It enables conflict-free local decision making, while avoiding communication overloading. One of the main issues in decomposition is the loss of flexibility due to decomposition. Here, flexibility roughly refers to the freedom in choosing suitable values for the variables in order to satisfy the constraints. In this paper, we concentrate on linear constraint systems and efficient decomposition techniques for them. Using a generalization of a flexibility metric developed for Simple Temporal Networks, we show how an efficient decomposition technique for linear constraint systems can be derived that minimizes the loss of flexibility. As a by-product of this decomposition technique, we propose an intuitively attractive flexibility metric for linear constraint systems where decomposition does not incur any loss of flexibility.
【Keywords】: Decomposition, Linear Programming, Scheduling
【Paper Link】 【Pages】:2388-2394
【Authors】: Fan Xie ; Martin Müller ; Robert Holte
【Abstract】: Greedy Best-First Search (GBFS) is a powerful algorithm at the heart of many state of the art satisficing planners. One major weakness of GBFS is its behavior in so-called uninformative heuristic regions (UHRs) - parts of the search space in which no heuristic provides guidance towards states with improved heuristic values. This work analyzes the problem of UHRs in planning in detail, and proposes a two level search framework as a solution. In Greedy Best-First Search with Local Exploration (GBFS-LE), a local exploration is started from within a global GBFS whenever the search seems stuck in UHRs. Two different local exploration strategies are developed and evaluated experimentally: Local GBFS (LS) and Local Random Walk Search (LRW). The two new planners LAMA-LS and LAMA-LRW integrate these strategies into the GBFS component of LAMA-2011. Both are shown to yield clear improvements in terms of both coverage and search time on standard International Planning Competition benchmarks, especially for domains that are proven to have large or un- bounded UHRs.
【Keywords】: Planning;Heuristic Search;Exploration;
【Paper Link】 【Pages】:2395-2402
【Authors】: Fan Xie ; Martin Müller ; Robert Holte ; Tatsuya Imai
【Abstract】: Utilizing multiple queues in Greedy Best-First Search (GBFS) has been proven to be a very effective approach to satisficing planning. Successful techniques include extra queues based on Helpful Actions (or Preferred Operators), as well as using Multiple Heuristics. One weakness of all standard GBFS algorithms is their lack of exploration. All queues used in these methods work as priority queues sorted by heuristic values. Therefore, misleading heuristics, especially early in the search process, can cause the search to become ineffective. Type systems, as introduced for heuristic search by Lelis et al, are a development of ideas for exploration related to the classic stratified sampling approach. The current work introduces a search algorithm that utilizes type systems in a new way – for exploration within a GBFS multiqueue framework in satisficing planning. A careful case study shows the benefits of such exploration for overcoming deficiencies of the heuristic. The proposed new baseline algorithm Type-GBFS solves almost 200 more problems than baseline GBFS over all International Planning Competition problems. Type-LAMA, a new planner which integrates Type-GBFS into LAMA-2011, solves 36.8 more problems than LAMA-2011.
【Keywords】: Planning;Heuristic Search;Exploration;
【Paper Link】 【Pages】:2403-2409
【Authors】: Udi Apsel ; Kristian Kersting ; Martin Mladenov
【Abstract】: Inference in large scale graphical models is an important task in many domains, and in particular probabilistic relational models (e.g. Markov logic networks). Such models often exhibit considerable symmetry, and it is a challenge to devise algorithms that exploit this symmetry to speed up inference. Recently, the automorphism group has been proposed to formalize mathematically what "exploiting symmetry" means. However, obtaining symmetry derived from automorphism is GI-hard, and consequently only a small fraction of the symmetry is easily available for effective employment. In this paper, we improve upon efficiency in two ways. First, we introduce the Cluster Signature Graph (CSG), a platform on which greater portions of the symmetries can be revealed and exploited. CSGs classify clusters of variables by projecting relations between cluster members onto a graph, allowing for the efficient pruning of symmetrical clusters even before their generation. Second, we introduce a novel framework based on CSGs for the Sherali-Adams hierarchy of linear program (LP) relaxations, dedicated to exploiting this symmetry for the benefit of tight Maximum A Posteriori (MAP) approximations. Combined with the pruning power of CSG, the framework quickly generates compact formulations for otherwise intractable LPs, as demonstrated by several empirical results.
【Keywords】:
【Paper Link】 【Pages】:2410-2416
【Authors】: Elias Bareinboim ; Jin Tian ; Judea Pearl
【Abstract】: Selection bias is caused by preferential exclusion of units from the samples and represents a major obstacle to valid causal and statistical inferences; it cannot be removed by randomized experiments and can rarely be detected in either experimental or observational studies. In this paper, we provide complete graphical and algorithmic conditions for recovering conditional probabilities from selection biased data. We also provide graphical conditions for recoverability when unbiased data is available over a subset of the variables. Finally, we provide a graphical condition that generalizes the backdoor criterion and serves to recover causal effects when the data is collected under preferential selection.
【Keywords】: selection bias; sampling bias; causal inference; causality; statistical inference
【Paper Link】 【Pages】:2417-2423
【Authors】: André da Motta Salles Barreto
【Abstract】: Fitted Q-iteration (FQI) stands out among reinforcement learning algorithms for its flexibility and ease of use. FQI can be combined with any regression method, and this choice determines the algorithm's statistical and computational properties. The combination of FQI with an ensemble of regression trees gives rise to an algorithm, FQIT, that is computationally efficient, scalable to high dimensional spaces, and robust to noise. Despite its nice properties and good performance in practice, FQIT also has some limitations: the fact that an ensemble of trees must be constructed (or updated) at each iteration confines the algorithm to the batch scenario. This paper aims to address this specific issue. Based on a strategy recently proposed in the literature, called the stochastic-factorization trick, we propose a modification of FQIT that makes it fully incremental, and thus suitable for on-line learning. We call the resulting method tree-based stochastic factorization (TBSF). We derive upper bounds for the difference between the value functions computed by FQIT and TBSF, and also show in which circumstances the approximations coincide. A series of computational experiments is presented to illustrate the properties of TBSF and to show its usefulness in practice, including a medical problem involving the treatment of patients infected with HIV.
【Keywords】: Reinforcement Learning; Markov Decision Processes; Fitted Q-Iteration; Regression Trees; Stochastic Factorization
【Paper Link】 【Pages】:2424-2430
【Authors】: Bryant Chen ; Jin Tian ; Judea Pearl
【Abstract】: In causal inference, all methods of model learning rely on testable implications, namely, properties of the joint distribution that are dictated by the model structure. These constraints, if not satisfied in the data, allow us to reject or modify the model. Most common methods of testing a linear structural equation model (SEM) rely on the likelihood ratio or chi-square test which simultaneously tests all of the restrictions implied by the model. Local constraints, on the other hand, offer increased power (Bollen and Pearl, 2013; McDonald, 2002) and, in the case of failure, provide the modeler with insight for revising the model specification. One strategy of uncovering local constraints in linear SEMs is to search for overidentified path coefficients. While these overidentifying constraints are well known, no method has been given for systematically discovering them. In this paper, we extend the half-trek criterion of (Foygel et al., 2012) to identify a larger set of structural coefficients and use it to systematically discover overidentifying constraints. Still open is the question of whether our algorithm is complete.
【Keywords】: structural equation models; causality; testable implications; causal models; testable implications; goodness of fit; verma constraints; overidentifying constraints; overidentifying restrictions; graphical models; half-trek criterion
【Paper Link】 【Pages】:2431-2438
【Authors】: Yetian Chen ; Jin Tian
【Abstract】: In this paper we develop an algorithm to find the k-best equivalence classes of Bayesian networks. Our algorithm is capable of finding much more best DAGs than the previous algorithm that directly finds the k-best DAGs (Tian, He and Ram 2010). We demonstrate our algorithm in the task of Bayesian model averaging. Empirical results show that our algorithm significantly outperforms the k-best DAG algorithm in both time and space to achieve the same quality of approximation. Our algorithm goes beyond the maximum-a-posteriori (MAP) model by listing the most likely network structures and their relative likelihood and therefore has important applications in causal structure discovery.
【Keywords】: Bayesian network; Bayesian model averaging; Markov equivalence class; Structure discovery
【Paper Link】 【Pages】:2439-2445
【Authors】: Xiannian Fan ; Changhe Yuan ; Brandon M. Malone
【Abstract】: A recent breadth-first branch and bound algorithm (BFBnB)for learning Bayesian network structures (Maloneet al. 2011) uses two bounds to prune the searchspace for better efficiency; one is a lower bound calculatedfrom pattern database heuristics, and the otheris an upper bound obtained by a hill climbing search.Whenever the lower bound of a search path exceeds theupper bound, the path is guaranteed to lead to suboptimalsolutions and is discarded immediately. This paperintroduces methods for tightening the bounds. Thelower bound is tightened by using more informed variablegroupings when creating the pattern databases, andthe upper bound is tightened using an anytime learningalgorithm. Empirical results show that these boundsimprove the efficiency of Bayesian network learning bytwo to three orders of magnitude.
【Keywords】: Structure Learning; Bayesian Network; heuristic search
【Paper Link】 【Pages】:2446-2452
【Authors】: Jesse Hostetler ; Alan Fern ; Tom Dietterich
【Abstract】: Monte Carlo tree search (MCTS) algorithms are a popular approach to online decision-making in Markov decision processes (MDPs). These algorithms can, however, perform poorly in MDPs with high stochastic branching factors. In this paper, we study state aggregation as a way of reducing stochastic branching in tree search. Prior work has studied formal properties of MDP state aggregation in the context of dynamic programming and reinforcement learning, but little attention has been paid to state aggregation in MCTS. Our main result is a performance loss bound for a class of value function-based state aggregation criteria in expectimax search trees. We also consider how to construct MCTS algorithms that operate in the abstract state space but require a simulator of the ground dynamics only. We find that trajectory sampling algorithms like UCT can be adapted easily, but that sparse sampling algorithms present difficulties. As a proof of concept, we experimentally confirm that state aggregation can improve the finite-sample performance of UCT.
【Keywords】: Monte Carlo tree search; State abstraction; Markov decision processes
【Paper Link】 【Pages】:2453-2459
【Authors】: Tushar Khot ; Sriraam Natarajan ; Jude W. Shavlik
【Abstract】: One-class classification approaches have been proposed in the literature to learn classifiers from examples of only one class. But these approaches are not directly applicable to relational domains due to their reliance on a feature vector or a distance measure. We propose a non-parametric relational one-class classification approach based on first-order trees. We learn a tree-based distance measure that iteratively introduces new relational features to differentiate relational examples. We update the distance measure so as to maximize the one-class classification performance of our model. We also relate our model definition to existing work on probabilistic combination functions and density estimation. We experimentally show that our approach can discover relevant features and outperform three baseline approaches.
【Keywords】: One-class classification; Statistical Relational Learning; Ensemble learning; Relational Distance Metric
【Paper Link】 【Pages】:2460-2466
【Authors】: Brandon Malone ; Kustaa Kangas ; Matti Järvisalo ; Mikko Koivisto ; Petri Myllymäki
【Abstract】: There are various algorithms for finding a Bayesian networkstructure (BNS) that is optimal with respect to a given scoring function. No single algorithm dominates the others in speed, and, given a problem instance, it is a priori unclear which algorithm will perform best and how fast it will solve the problem. Estimating the runtimes directly is extremely difficult as they are complicated functions of the instance. The main contribution of this paper is characterization of the empirical hardness of an instance for a given algorithm based on a novel collection of non-trivial, yet efficiently computable features. Our empirical results, based on the largest evaluation of state-of-the-art BNS learning algorithms to date, demonstrate that we can predict the runtimes to a reasonable degree of accuracy, and effectively select algorithms that perform well on a particular instance. Moreover, we also show how the results can be utilized in building a portfolio algorithm that combines several individual algorithms in an almost optimal manner.
【Keywords】:
【Paper Link】 【Pages】:2467-2475
【Authors】: Mathias Niepert ; Guy Van den Broeck
【Abstract】: Exchangeability is a central notion in statistics and probability theory. The assumption that an infinite sequence of data points is exchangeable is at the core of Bayesian statistics. However, finite exchangeability as a statistical property that renders probabilistic inference tractable is less well-understood. We develop a theory of finite exchangeability and its relation to tractable probabilistic inference. The theory is complementary to that of independence and conditional independence. We show that tractable inference in probabilistic models with high treewidth and millions of variables can be explained with the notion of finite (partial) exchangeability. We also show that existing lifted inference algorithms implicitly utilize a combination of conditional independence and partial exchangeability.
【Keywords】: tractability; exchangeability; efficient inference
【Paper Link】 【Pages】:2476-2482
【Authors】: Aditya V. Nori ; Chung-Kil Hur ; Sriram K. Rajamani ; Selva Samuel
【Abstract】: We present a new Markov Chain Monte Carlo (MCMC) sampling algorithm for probabilistic programs. Our approach and tool, called R2, has the unique feature of employing program analysis in order to improve the efficiencyof MCMC sampling. Given an input program P, R2 propagates observations in P backwards to obtaina semantically equivalent program P' in which every probabilistic assignment is immediately followed by an observe statement. Inference is performed by a suitably modified version of the Metropolis-Hastings algorithm that exploits the structure of the program P'. This has the overall effect of preventing rejections due to program executions that fail to satisfy observations in P. We formalize the semantics of probabilistic programs and rigorously prove the correctness of R2. We also empirically demonstrate the effectiveness of R2—in particular, we show that R2 is able to produce results of similar quality as the CHURCH and STAN probabilistic programming tools with much shorter execution time.
【Keywords】: Probabilistic Programming, Program analysis, Sampling
【Paper Link】 【Pages】:2483-2489
【Authors】: Pedro A. Ortega ; Daniel D. Lee
【Abstract】: Recently, there has been a growing interest in modeling planning with information constraints. Accordingly, an agent maximizes a regularized expected utility known as the free energy, where the regularizer is given by the information divergence from a prior to a posterior policy. While this approach can be justified in various ways, including from statistical mechanics and information theory, it is still unclear how it relates to decision-making against adversarial environments. This connection has previously been suggested in work relating the free energy to risk-sensitive control and to extensive form games. Here, we show that a single-agent free energy optimization is equivalent to a game between the agent and an imaginary adversary. The adversary can, by paying an exponential penalty, generate costs that diminish the decision maker's payoffs. It turns out that the optimal strategy of the adversary consists in choosing costs so as to render the decision maker indifferent among its choices, which is a definining property of a Nash equilibrium, thus tightening the connection between free energy optimization and game theory.
【Keywords】: bounded rationality; free energy; game theory; Legendre-Fenchel transform
【Paper Link】 【Pages】:2490-2496
【Authors】: Joris Renkens ; Angelika Kimmig ; Guy Van den Broeck ; Luc De Raedt
【Abstract】: Probabilistic inference can be realized using weighted model counting. Despite a lot of progress, computing weighted model counts exactly is still infeasible for many problems of interest, and one typically has to resort to approximation methods. We contribute a new bounded approximation method for weighted model counting based on probabilistic logic programming principles. Our bounded approximation algorithm is an anytime algorithm that provides lower and upper bounds on the weighted model count. An empirical evaluation on probabilistic logic programs shows that our approach is effective in many cases that are currently beyond the reach of exact methods.
【Keywords】: Probabilistic Logic Programming;Bounded Approximate Inference;Weighted Model Counting
【Paper Link】 【Pages】:2497-2504
【Authors】: Parag Singla ; Aniruddh Nath ; Pedro M. Domingos
【Abstract】: Many AI applications need to explicitly represent relational structure as well as handle uncertainty. First order probabilistic models combine the power of logic and probability to deal with such domains. A naive approach to inference in these models is to propositionalize the whole theory and carry out the inference on the ground network. Lifted inference techniques (such as lifted belief propagation; Singla and Domingos 2008) provide a more scalable approach to inference by combining together groups of objects which behave identically. In many cases, constructing the lifted network can itself be quite costly. In addition, the exact lifted network is often very close in size to the fully propositionalized model. To overcome these problems, we present approximate lifted inference, which groups together similar but distinguishable objects and treats them as if they were identical. Early stopping terminates the execution of the lifted network construction at an early stage resulting in a coarser network. Noise-tolerant hypercubes allow for marginal errors in the representation of the lifted network itself. Both of our algorithms can significantly speed up the process of lifted network construction as well as result in much smaller models. The coarseness of the approximation can be adjusted depending on the accuracy required, and we can bound the resulting error. Extensive evaluation on six domains demonstrates great efficiency gains with only minor (or no) loss in accuracy.
【Keywords】: Statistical Relational AI; Lifted Inference; Approximate Inference
【Paper Link】 【Pages】:2505-2512
【Authors】: Pradeep Varakantham ; Yossiri Adulyasak ; Patrick Jaillet
【Abstract】: In this paper, we solve cooperative decentralized stochastic planning problems, where the interactions between agents (specified using transition and reward functions) are dependent on the number of agents (and not on the identity of the individual agents) involved in the interaction. A collision of robots in a narrow corridor, defender teams coordinating patrol activities to secure a target, etc. are examples of such anonymous interactions. Formally, we consider problems that are a subset of the well known Decentralized MDP (DEC-MDP) model, where the anonymity in interactions is specified within the joint reward and transition functions. In this paper, not only do we introduce a general model model called D-SPAIT to capture anonymity in interactions, but also provide optimization based optimal and local-optimal solutions for generalizable sub-categories of D-SPAIT.
【Keywords】: Decentralized Planning with Uncertainty, DEC-MDP, Stochastic Routing, Selfish Routing
【Paper Link】 【Pages】:2513-2519
【Authors】: Tiago Veiga ; Matthijs T. J. Spaan ; Pedro U. Lima
【Abstract】: Partially observable Markov decision processes (POMDPs) provide a principled mathematical framework for modeling autonomous decision-making problems. A POMDP solution is often represented by a value function comprised of a set of vectors. In the case of factored models, the size of these vectors grows exponentially with the number of state factors, leading to scalability issues. We consider an approximate value function representation based on a linear combination of basis functions. In particular, we present a backup operator that can be used in any point-based POMDP solver. Furthermore, we show how under certain conditions independence between observation factors can be exploited for large computational gains. We experimentally verify our contributions and show that they have the potential to improve point-based methods in policy quality and solution size.
【Keywords】:
【Paper Link】 【Pages】:2520-2526
【Authors】: Abdeslam Boularias ; James Andrew Bagnell ; Anthony Stentz
【Abstract】: Manipulating natural objects of irregular shapes, such as rocks, is an essential capability of robots operating in outdoor environments. Physics-based simulators are commonly used to plan stable grasps for man-made objects. However, planning is an expensive process that is based on simulating hand and object trajectories in different configurations, and evaluating the outcome of each trajectory. This problem is particularly concerning when the objects are irregular or cluttered, because the space of feasible grasps is significantly smaller, and more configurations need to be evaluated before finding a good one. In this paper, we first present a learning technique for fast detection of an initial set of potentially stable grasps in a cluttered scene. The best detected grasps are further optimized by fine-tuning the configuration of the hand in simulation. To reduce the computational burden of this last operation, we model the outcomes of the grasps as a Gaussian Process, and use an entropy-search method in order to focus the optimization on regions where the best grasp is most likely to be. This approach is tested on the task of clearing piles of real, unknown, rock debris with an autonomous robot. Empirical results show a clear advantage of the proposed approach when the time window for decision is short.
【Keywords】: Robotic Grasping; Robotic Manipulation; Robot Learning
【Paper Link】 【Pages】:2527-2533
【Authors】: Jennifer Elisabeth Buehler ; Maurice Pagnucco
【Abstract】: In heterogeneous multi-robot teams, robustness and flexibility are increased by the diversity of the robots, each contributing different capabilities. Yet platform-independence is desirable when planning actions for the various robots. We propose a platform-independent model of robot capabilities which we use as a planning domain. We extend existing planning techniques to support two requirements: generating new objects during planning; and, required concurrency of actions due to data flow which can be cyclic. The first requires online action instantiation, the second a small extension of the Planning Domain Definition Language (PDDL): allowing predicates in continuous effects. We evaluate the planner on benchmark domains and present results on an example object transportation task in simulation.
【Keywords】: Robotics; Multi Robot Systems; Planning and Scheduling; Robot Task Planning; Robot Capabilities
【Paper Link】 【Pages】:2534-2541
【Authors】: Jared Glover ; Charlotte Zhu
【Abstract】: We present a ping-pong-playing robot that learns to improve its swings with human advice. Our method learns a reward function over the joint space of task and policy parameters T×P, so the robot can explore policy space more intelligently in a way that trades off exploration vs. exploitation to maximize the total cumulative reward over time. Multimodal stochastic polices can also easily be learned with this approach when the reward function is multimodal in the policy parameters. We extend the recently-developed Gaussian Process Bandit Optimization framework to include exploration-bias advice from human domain experts, using a novel algorithm called Exploration Bias with Directional Advice (EBDA).
【Keywords】: Learning from Advice
【Paper Link】 【Pages】:2542-2548
【Authors】: Bradford Heap ; Maurice Pagnucco
【Abstract】: In multi-robot task allocation problems with in-schedule dependencies, tasks with high costs have a large influence on the total time required for a team of robots to complete all tasks. We reduce this influence by calculating a novel task cost dispersion value that measures robots' collective preference for each task. By modifying the winner determination phase of sequential single-item auctions, our approach inspects the bids for every task to identify tasks which robots collectively consider to be high cost and ensures these tasks are allocated prior to other tasks.Our empirical results show this method provides a significant reduction in the total time required to complete all tasks.
【Keywords】: Multi-Robot Systems; Multi-Agent Systems; Task Allocation; Auctions
【Paper Link】 【Pages】:2549-2555
【Authors】: Ryan Luna ; Morteza Lahijanian ; Mark Moll ; Lydia E. Kavraki
【Abstract】: A framework capable of computing optimal control policies for a continuous system in the presence of both action and environment uncertainty is presented in this work. The framework decomposes the planning problem into two stages: an offline phase that reasons only over action uncertainty and an online phase that quickly reacts to the uncertain environment. Offline, a bounded-parameter Markov decision process (BMDP) is employed to model the evolution of the stochastic system over a discretization of the environment. Online, an optimal control policy over the BMDP is computed. Upon the discovery of an unknown environment feature during policy execution, the BMDP is updated and the optimal control policy is efficiently recomputed. Depending on the desired quality of the control policy, a suite of methods is presented to incorporate new information into the BMDP with varying degrees of detail online. Experiments confirm that the framework recomputes high-quality policies in seconds and is orders of magnitude faster than existing methods.
【Keywords】:
【Paper Link】 【Pages】:2556-2563
【Authors】: Cynthia Matuszek ; Liefeng Bo ; Luke Zettlemoyer ; Dieter Fox
【Abstract】: As robots become more ubiquitous, it is increasingly important for untrained users to be able to interact with them intuitively. In this work, we investigate how people refer to objects in the world during relatively unstructured communication with robots. We collect a corpus of deictic interactions from users describing objects, which we use to train language and gesture models that allow our robot to determine what objects are being indicated. We introduce a temporal extension to state-of-the-art hierarchical matching pursuit features to support gesture understanding, and demonstrate that combining multiple communication modalities more effectively captures user intent than relying on a single type of input. Finally, we present initial interactions with a robot that uses the learned models to follow commands while continuing to learn from user input.
【Keywords】: Gesture, Natural Language, Human-Robot Interaction
【Paper Link】 【Pages】:2564-2570
【Authors】: Tayyab Naseer ; Luciano Spinello ; Wolfram Burgard ; Cyrill Stachniss
【Abstract】: Image-based localization is an important problem in robotics and an integral part of visual mapping and navigation systems. An approach to robustly match images to previously recorded ones must be able to cope with seasonal changes especially when it is supposed to work reliably over long periods of time. In this paper, we present a novel approach to visual localization of mobile robots in outdoor environments, which is able to deal with substantial seasonal changes. We formulate image matching as a minimum cost flow problem in a data association graph to effectively exploit sequence information. This allows us to deal with non-matching image sequences that result from temporal occlusions or from visiting new places. We present extensive experimental evaluations under substantial seasonal changes. Our approach achieves accurate matching across seasons and outperforms existing state-of-the-art methods such as FABMAP2 and SeqSLAM.
【Keywords】: robotics; visual localization ; seasons
【Paper Link】 【Pages】:2571-2577
【Authors】: Markus Sebastian Schwenk ; Tiago Stegun Vaquero ; Goldie Nejat ; Kai Oliver Arras
【Abstract】: In this paper we address the planning problem of a robot searching for multiple residents in a retirement home in order to remind them of an upcoming multi-person recreational activity before a given deadline. We introduce a novel Multi-User Schedule Based (M-USB) Search approach which generates a high-level-plan to maximize the number of residents that are found within the given time frame. From the schedules of the residents, the layout of the retirement home environment as well as direct observations by the robot, we obtain spatio-temporal likelihood functions for the individual residents. The main contribution of our work is the development of a novel approach to compute a reward to find a search plan for the robot using: 1) the likelihood functions, 2) the availabilities of the residents, and 3) the order in which the residents should be found. Simulations were conducted on a floor of a real retirement home to compare our proposed M-USB Search approach to a Weighted Informed Walk and a Random Walk. Our results show that the proposed M-USB Search finds residents in a shorter amount of time by visiting fewer rooms when compared to the other approaches.
【Keywords】: Assistive Robots; Multi-Person Search; Retirement Home Environment; Resident Schedules
【Paper Link】 【Pages】:2578-2584
【Authors】: Timothy Wiley ; Claude Sammut ; Ivan Bratko
【Abstract】: This paper resolves previous problems in the Multi-Strategy architecture for online learning of robotic behaviours. The hybrid method includes a symbolic qualitative planner that constructs an approximate solution to a control problem. The approximate solution provides constraints for a numerical optimisation algorithm, which is used to refine the qualitative plan into an operational policy. Introducing quantitative constraints into the planner gives previously unachievable domain independent reasoning. The method is demonstrated on a multi-tracked robot intended for urban search and rescue.
【Keywords】: Robotics;Qualitative Reasoning;Qualitative Planning;Machine Learning
【Paper Link】 【Pages】:2585-2593
【Authors】: Nuo Xu ; Kian Hsiang Low ; Jie Chen ; Keng Kiat Lim ; Etkin Baris Ozgul
【Abstract】: Central to robot exploration and mapping is the task of persistent localization in environmental fields characterized by spatially correlated measurements. This paper presents a Gaussian process localization (GP-Localize) algorithm that, in contrast to existing works, can exploit the spatially correlated field measurements taken during a robot's exploration (instead of relying on prior training data) for efficiently and scalably learning the GP observation model online through our proposed novel online sparse GP. As a result, GP-Localize is capable of achieving constant time and memory (i.e., independent of the size of the data) per filtering step, which demonstrates the practical feasibility of using GPs for persistent robot localization and autonomy. Empirical evaluation via simulated experiments with real-world datasets and a real robot experiment shows that GP-Localize outperforms existing GP localization algorithms.
【Keywords】: Robot localization; Gaussian process; Online learning
【Paper Link】 【Pages】:2594-2600
【Authors】: Carlos Ansótegui ; Yuri Malitsky ; Meinolf Sellmann
【Abstract】: Our objective is to boost the state-of-the-art performance in MaxSATsolving. To this end, we employ the instance-specific algorithmconfigurator ISAC, and improve it with the latest inportfolio technology. Experimental results on SAT show that thiscombination marks a significant step forward in our ability to tunealgorithms instance-specifically. We then apply the new methodology toa number of MaxSAT problem domains and show that the resulting solversconsistently outperform the best existing solvers on the respectiveproblem families. In fact, the solvers presented here were independentlyevaluated at the 2013 MaxSAT Evaluation where they won six of the elevencategories.
【Keywords】: Algorithm Selection; MaxSAT; Algorithm Configuration
【Paper Link】 【Pages】:2601-2607
【Authors】: Amine Balafrej ; Christian Bessiere ; El-Houssine Bouyakhf ; Gilles Trombettoni
【Abstract】: Singleton-based consistencies have been shown to dramatically improve the performance of constraint solvers on some difficult instances. However, they are in general too expensive to be applied exhaustively during the whole search. In this paper, we focus on partition-one-AC, a singleton-based consistency which, as opposed to singleton arc consistency, is able to prune values on all variables when it performs singleton tests on one of them. We propose adaptive variants of partition-one-AC that do not necessarily run until having proved the fixpoint. The pruning can be weaker than the full version but the computational effort can be significantly reduced. Our experiments show that adaptive Partition-one-AC can obtain significant speed-ups over arc consistency and over the full version of partition-one-AC.
【Keywords】: constraint programming, Consistencies, Adaptive
【Paper Link】 【Pages】:2608-2615
【Authors】: Paul Beame ; Ashish Sabharwal
【Abstract】: Propositional satisfiability (SAT) solvers based on conflict directed clause learning (CDCL) implicitly produce resolution refutations of unsatisfiable formulas. The precise class of formulas for which they can produce polynomial size refutations has been the subject of several studies, with special focus on the clause learning aspect of these solvers. The results, however, assume the use of non-standard and non-asserting learning schemes, or rely on polynomially many restarts for simulating individual steps of a resolution refutation, or work with a theoretical model that significantly deviates from certain key aspects of all modern CDCL solvers such as learning only one asserting clause from each conflict and other techniques such as conflict guided backjumping and phase saving. We study non-restarting CDCL solvers that learn only one asserting clause per conflict and show that, with simple preprocessing that depends only on the number of variables of the input formula, such solvers can polynomially simulate resolution. We show, moreover, that this preprocessing allows one to convert any CDCL solver to one that is non-restarting.
【Keywords】: clause learning; restarts; preprocessing
【Paper Link】 【Pages】:2616-2622
【Authors】: Nicolas Beldiceanu ; Pierre Flener ; Justin Pearson ; Pascal Van Hentenryck
【Abstract】: Constraints over finite sequences of variables are ubiquitous in sequencing and timetabling. This led to general modelling techniques and generic propagators, often based on deterministic finite automata (DFA) and their extensions. We consider counter-DFAs (cDFA), which provide concise models for regular counting constraints, that is constraints over the number of times a regular-language pattern occurs in a sequence. We show how to enforce domain consistency in polynomial time for at-most and at-least regular counting constraints based on the frequent case of a cDFA with only accepting states and a single counter that can be increased by transitions. We also show that the satisfaction of exact regular counting constraints is NP-hard and that an incomplete propagator for exact regular counting constraints is faster and provides more pruning than the existing propagator from (Beldiceanu, Carlsson, and Petit 2004). Finally, by avoiding the unrolling of the cDFA used by COSTREGULAR, the space complexity reduces from O(n · |Σ| · |Q|) to O(n · (|Σ| + |Q|)), where Σ is the alphabet and Q the state set of the cDFA.
【Keywords】:
【Paper Link】 【Pages】:2623-2629
【Authors】: Shaowei Cai ; Chuan Luo ; John Thornton ; Kaile Su
【Abstract】: Partial MaxSAT (PMS) is a generalization to SAT and MaxSAT. Many real world problems can be encoded into PMS in a more natural and compact way than SAT and MaxSAT. In this paper, we propose new ideas for local search for PMS, which mainly rely on the distinction between hard and soft clauses. We use these ideas to develop a local search PMS algorithm called {\it Dist}. Experimental results on PMS benchmarks from MaxSAT Evaluation 2013 show that {\it Dist} significantly outperforms state-of-the-art PMS algorithms, including both local search algorithms and complete ones, on random and crafted benchmarks. For the industrial benchmark, {\it Dist} dramatically outperforms previous local search algorithms and is comparable with complete algorithms.
【Keywords】: Partial MaxSAT; Local Search
【Paper Link】 【Pages】:2630-2636
【Authors】: Clément Carbonnel ; Gilles Trombettoni ; Philippe Vismara ; Gilles Chabert
【Abstract】: Given a set of axis-parallel n-dimensional boxes, the q-intersection is defined as the smallest box encompassing all the points that belong to at least q boxes. Computing the q-intersection is a combinatorial problem that allows us to handle robust parameter estimation with a numerical constraint programming approach. The q-intersection can be viewed as a filtering operator for soft constraints that model measurements subject to outliers. This paper highlights the equivalence of this operator with the search of q-cliques in a graph whose boxicity is bounded by the number of variables in the constraint network. We present a computational study of the q-intersection. We also propose a fast heuristic and a sophisticated exact q-intersection algorithm. First experiments show that our exact algorithm outperforms the existing one while our heuristic performs an efficient filtering on hard problems.
【Keywords】: q-intersection; soft numerical constraints; parameter estimation; intersection graph
【Paper Link】 【Pages】:2637-2643
【Authors】: Hamed Fahimi ; Claude-Guy Quimper
【Abstract】: We present three new filtering algorithms for the Disjunctive constraint that all have a linear running time complexity in the number of tasks. The first algorithm filters the tasks according to the rules of the time tabling. The second algorithm performs an overload check that could also be used for the Cumulative constraint. The third algorithm enforces the rules of detectable precedences. The two last algorithms use a new data structure that we introduce and that we call the time line. This data structure provides many constant time operations that were previously implemented in logarithmic time by the Theta-tree data structure. Experiments show that these new algorithms are competitive even for a small number of tasks and outperform existing algorithms as the number of tasks increases.
【Keywords】: Constraint Programming; Scheduling; Global Constraint; Disjunctive
【Paper Link】 【Pages】:2644-2651
【Authors】: Alexander Feldman ; Gregory M. Provan
【Abstract】: Fault diagnosis of analogue linear systems poses many challenges, such as the size of the search space that must be explored and the possibility of simulation instabilities introduced by particular fault classes. We study a novel algorithm that addresses both problems. This algorithm dynamically modifies the simulation model during diagnosis by pruning parametrized components that cause discontinuity in the model. We provide a theoretical framework for predicting the speedups, which depends on the topology of the model. We empirically validate the theoretical predictions through extensive experimentation on a benchmark of circuits.
【Keywords】: diagnosis, model-based diagnosis, automated reasoning, simulation, numerical methods
【Paper Link】 【Pages】:2652-2658
【Authors】: Serge Gaspers ; Neeldhara Misra ; Sebastian Ordyniak ; Stefan Szeider ; Stanislav Zivny
【Abstract】: Backdoor sets represent clever reasoning shortcuts through the search space for SAT and CSP. By instantiating the backdoor variables one reduces the given instance to several easy instances that belong to a tractable class.The overall time needed to solve the instance is exponential in the size of the backdoor set, hence it is a challenging problem to find a small backdoor set if one exists; over the last years this problem has been subject of intensive research. In this paper we extend the classical notion of a strong backdoor set by allowing that different instantiations of the backdoor variables result in instances that belong to different base classes; the union of the base classes forms a heterogeneous base class. Backdoor sets to heterogeneous base classes can be much smaller than backdoor sets to homogeneous ones, hence they are much more desirable but possibly harder to find. We draw a detailed complexity landscape for the problem of detecting strong backdoor sets into heterogeneous base classes for SAT and CSP. We provide algorithms that establish fixed-parameter tractability under natural parameterizations, and we contrast the tractability results with hardness results that pinpoint the theoretical limits. Our results apply to the current state-of-the-art of tractable classes of CSP and SAT that are definable by restricting the constraint language.
【Keywords】: backdoor set;parameterized complexity;constraint satisfaction;satisfiability
【Paper Link】 【Pages】:2659-2665
【Authors】: Stella Giannakopoulou ; Charalampos Nikolaou ; Manolis Koubarakis
【Abstract】: The problem of checking the consistency of spatial calculi that contain both unknown and known entities (constants, i.e., real geometries) has recently been studied. Until now, all the approaches are theoretical and no implementation has been proposed. In this paper we present the first reasoner that takes as input RCC-5 or RCC-8 networks with variables and constants and decides their consistency. We investigate the performance of the reasoner experimentally using real-world networks and show that we can achieve significantly better times by geometry simplification and parallelization.
【Keywords】: Qualitative spatial reasoning; Constraint Satisfaction Problems; Landmarks; Reasoning
【Paper Link】 【Pages】:2666-2673
【Authors】: Éric Grégoire ; Jean-Marie Lagniez ; Bertrand Mazure
【Abstract】: The concepts of MSS (Maximal Satisfiable Subset) andCoMSS (also called Minimal Correction Subset) playa key role in many A.I. approaches and techniques. Inthis paper, a novel algorithm for partitioning a BooleanCNF formula into one MSS and the correspondingCoMSS is introduced. Extensive empirical evaluationshows that it is more robust and more efficient on mostinstances than currently available techniques.
【Keywords】: SAT; coMSS; MSS
【Paper Link】 【Pages】:2674-2679
【Authors】: Renaud Hartert ; Pierre Schaus
【Abstract】: Bi-Objective Combinatorial Optimization problems are ubiquitous in real-world applications and designing approaches to solve them efficiently is an important research area of Artificial Intelligence. In Constraint Programming, the recently introduced bi-objective Pareto constraint allows one to solve bi-objective combinatorial optimization problems exactly. Using this constraint, every non-dominated solution is collected in a single tree-search while pruning sub-trees that cannot lead to a non-dominated solution. This paper introduces a simpler and more efficient filtering algorithm for the bi-objective Pareto constraint. The efficiency of this algorithm is experimentally confirmed on classical bi-objective benchmarks.
【Keywords】: Constraint programming, global constraint, bi-objective optimization
【Paper Link】 【Pages】:2680-2687
【Authors】: Yoonheui Kim ; Victor R. Lesser
【Abstract】: In this paper we propose a novel DCOP algorithm, called DJAO, that is able toefficiently find a solution with low communication overhead; this algorithm can be used for optimal and bounded approximate solutions by appropriately setting the error bounds. Our approach builds on distributed junction trees used in Action-GDL to represent independence relationsamong variables. We construct an AND/OR search space based on these junction trees.This new type of search space results in higher degrees for each OR node, consequently yielding a more efficient search graph in the distributed settings. DJAO uses a branch-and-bound search algorithm to distributedly find solutions within this search graph. We introduce heuristics to compute the upper and lower boundestimates that the search starts with, which is integral to our approach for reducing communication overhead. We empirically evaluate our approach in various settings.
【Keywords】: Distributed constraint reasoning; DCOP; AND/OR search
【Paper Link】 【Pages】:2688-2694
【Authors】: Jean-Marie Lagniez ; Pierre Marquis
【Abstract】: This paper is concerned with preprocessing techniques for propositional model counting. We have implemented a preprocessor which includes many elementary preprocessing techniques, including occurrence reduction, vivification, backbone identification, as well as equivalence, AND and XOR gate identification and replacement. We performed intensive experiments, using a huge number of benchmarks coming from a large number of families. Two approaches to model counting have been considered downstream: ”direct” model counting using Cachet and compilation-based model counting, based on the C2D compiler. The experimental results we have obtained show that our preprocessor is both efficient and robust.
【Keywords】: propositional logic; model counting; preprocessing
【Paper Link】 【Pages】:2695-2702
【Authors】: Jimmy H. M. Lee ; Zichen Zhu
【Abstract】: The paper proposes a dynamic method, Recursive SBDS(ReSBDS), for efficient partial symmetry breaking. Wefirst demonstrate how (partial) Symmetry BreakingDuring Search (SBDS) misses important pruning opportunitieswhen given only a subset of symmetries tobreak. The investigation pinpoints the culprit and in turnsuggests rectification. The main idea is to add extra conditionalconstraints during search recursively to prunealso symmetric nodes of some pruned subtrees. Thus,ReSBDS can break extra symmetry compositions, butis carefully designed to break only the ones that areeasy to identify and inexpensive to break. We presenttheorems to guarantee the soundness and terminationof our approach, and compare our method with popularstatic and dynamic methods. When the variable (value)heuristic is static, ReSBDS is also complete in eliminatingall interchangeable variables (values) given only thegenerator symmetries. Extensive experimentations confirmthe efficiency of ReSBDS, when compared againststate of the art methods.
【Keywords】:
【Paper Link】 【Pages】:2703-2709
【Authors】: Chuan Luo ; Shaowei Cai ; Wei Wu ; Kaile Su
【Abstract】: Stochastic local search (SLS) algorithms have shown effectiveness on satisfiable instances of the Boolean satisfiability (SAT) problem. However, their performance is still unsatisfactory on random k-SAT at the phase transition, which is of significance and is one of the empirically hardest distributions of SAT instances. In this paper, we propose a new heuristic called DCCA, which combines two configuration checking (CC) strategies with different definitions of configuration in a novel way. We use the DCCA heuristic to design an efficient SLS solver for SAT dubbed DCCASat. The experiments show that the DCCASat solver significantly outperforms a number of state-of-the-art solvers on extensive random k-SAT benchmarks at the phase transition. Moreover, DCCASat shows good performance on structured benchmarks, and a combination of DCCASat with a complete solver achieves state-of-the-art performance on structured benchmarks.
【Keywords】: Double Configuration Checking; Stochastic Local Search; Satisfiability
【Paper Link】 【Pages】:2710-2716
【Authors】: Jean-Noël Monette ; Pierre Flener ; Justin Pearson
【Abstract】: Constraints over variable sequences are ubiquitous and many of their propagators have been inspired by dynamic programming (DP). We propose a conceptual framework for designing such propagators: pruning rules, in a functional notation, are refined upon the application of transformation operators to a DP-style formulation of a constraint; a representation of the (tuple) variable domains is picked; and a control of the pruning rules is picked.
【Keywords】:
【Paper Link】 【Pages】:2717-2723
【Authors】: Nina Narodytska ; Fahiem Bacchus
【Abstract】: Core-guided approaches to solving MAXSAT have proved to be effective on industrial problems. These approaches solve a MAXSAT formula by building a sequence of SAT formulas, where in each formula a greater weight of soft clauses can be relaxed. The soft clauses are relaxed via the addition of blocking variables, and the total weight of soft clauses that can be relaxed is limited by placing constraints on the blocking variables. In this work we propose an alternative approach. Our approach also builds a sequence of new SAT formulas. However, these formulas are constructed using MAXSAT resolution, a sound rule of inference for MAXSAT. MAXSAT resolution can in the worst case cause a quadratic blowup in the formula, so we propose a new compressed version of MAXSAT resolution. Using compressed MAXSAT resolution our new core-guided solver improves the state-of-theart, solving significantly more problems than other state-ofthe-art solvers on the industrial benchmarks used in the 2013 MAXSAT Solver Evaluation.
【Keywords】: Maximum Satisfiability, MAXSAT resolution
【Paper Link】 【Pages】:2724-2730
【Authors】: Charalampos Nikolaou ; Manolis Koubarakis
【Abstract】: We present a new reasoner for RCC-8 constraint networks, called gp-rcc8, that is based on the patchwork property of path-consistent tractable RCC-8 networks and graph partitioning. We compare gp-rcc8 with state of the art reasoners that are based on constraint propagation and backtracking search as well as one that is based on graph partitioning and SAT solving. Our evaluation considers very large real-world RCC-8 networks and medium-sized synthetic ones, and shows that gp-rcc8 outperforms the other reasoners for these networks, while it is less efficient for smaller networks.
【Keywords】: qualitative spatial reasoning; consistency checking; graph partitioning
【Paper Link】 【Pages】:2731-2737
【Authors】: Alexandre Papadopoulos ; Pierre Roy ; François Pachet
【Abstract】: Markov processes are widely used to generate sequences that imitate a given style, using random walk. Random walk generates sequences by iteratively concatenating states to prefixes of length equal or less than the given Markov order}. However, at higher orders, Markov chains tend to replicate chunks of the corpus with a size possibly higher than the order, a primary form of plagiarism. The Markov order defines a maximum length for training but not for generation. In the framework of constraint satisfaction (CSP), we introduce MaxOrder. This global constraint ensures that generated sequences do not include chunks larger than a given maximum order. We exhibit an automaton that recognises the solution set, with a size linear in the size of the corpus. We propose a linear-time procedure to generate this automaton from a corpus and a given max order. We then use this automaton to achieve generalised arc consistency for the MaxOrder constraint, holding on a sequence of size n, in O(n.T) time, where T is the size of the automaton. We illustrate our approach by generating text sequences from text corpora with a maximum order guarantee, effectively controlling plagiarism.
【Keywords】: markov chains; constraint programming; music generation; automatic content generation; plagiarism; max order
【Paper Link】 【Pages】:2738-2745
【Authors】: Ethan L. Schreiber ; Richard E. Korf
【Abstract】: The NP-hard number-partitioning problem is to separate a multiset S of n positive integers into k subsets, such that the largest sum of the integers assigned to any subset is minimized. The classic application is scheduling a set of n jobs with different run times onto k identical machines such that the makespan, the time to complete the schedule, is minimized. We present a new algorithm, cached iterative weakening (CIW), for solving this problem optimally. It incorporates three ideas distinct from the previous state of the art: it explores the search space using iterative weakening instead of branch and bound; generates feasible subsets once and caches them instead of at each node of the search tree; and explores subsets in cardinality order instead of an arbitrary order. The previous state of the art is represented by three different algorithms depending on the values of n and k. We provide one algorithm which outperforms all previous algorithms for k >= 4. Our run times are up to two orders of magnitude faster.
【Keywords】: Heuristic Search and Optimization; Heuristic Search; Optimization; Search (General/Other)
【Paper Link】 【Pages】:2746-2752
【Authors】: Liang Du ; Haibin Ling
【Abstract】: Joint learning of similar tasks has been a popular trend in visual recognition and proven to be beneficial. Between-task similarity often provides useful cues, such as feature sharing, for learning visual classifiers. By contrast, the competition relationship between visual recognition tasks (e.g., content independent writer identification and handwriting recognition) remains largely under-explored. A key challenge in visual recognition is to select the most discriminating features and remove irrelevant features related to intra-class variations. With the help of auxiliary competing tasks, we can identify such features within a joint learning model exploiting the competition relationship.Motivated by this intuition, we propose a novel way to exploit competition relationship for solving visual recognition problems. Specifically, given a target task and its competing tasks, we jointly model them by a generalized additive regression model with a competition constraint. This constraint effectively discourages choosing of irrelevant features (weak learners) that support the auxiliary competing tasks. We name the proposed algorithm CompBoost. In our study, CompBoost is applied to two visual recognition applications: (1) content-independent writer identification from handwriting scripts by exploiting competing tasks of handwriting recognition, and (2) actor-independent facial expression recognition by exploiting competing tasks of face recognition. In both experiments our approach demonstrates promising performance gains by exploiting the between-task competition.
【Keywords】: Transfer Learning; Visual Recognition; Competing Tasks
【Paper Link】 【Pages】:2753-2759
【Authors】: Longwen Gao ; Shuigeng Zhou
【Abstract】: Non-rigid shape comparison based on manifold embeddingusing Generalized Multidimensional Scaling(GMDS) has attracted much attention for its highaccuracy. However, this method requires that shape surfaceis not elastic. In other words, it is sensitive totopological transformations such as stretching and compressing.To tackle this problem, we propose a new approachthat constructs a high-dimensional space to embedthe manifolds of shapes based on sparse representation,which is able to completely withstand rigid transformationsand considerably tolerate topological transformations.Experiments on TOSCA shapes validate theproposed approach.
【Keywords】: Sparse representation; Manifold embedding
【Paper Link】 【Pages】:2760-2766
【Authors】: Muhammad Wajahat Hussain ; Javier Civera ; Luis Montano
【Abstract】: Extracting the 3D geometry plays an important part in scene understanding. Recently, robust visual descriptors are proposed for extracting the indoor scene layout from a passive agent’s perspective, specifically from a single image. Their robustness is mainly due to modelling the physical interaction of the underlying room geometry with the objects and the humans present in the room. In this work we add the physical constraints coming from acoustic echoes, generated by an audio source, to this visual model. Our audio-visual 3D geometry descriptor improves over the state of the art in passive perception models as we show in our experiments.
【Keywords】: layout, audio-visual scene understanding
【Paper Link】 【Pages】:2767-2772
【Authors】: Jianqiu Ji ; Jianmin Li ; Shuicheng Yan ; Qi Tian ; Bo Zhang
【Abstract】: Linear subspace is an important representation for many kinds of real-world data in computer vision and pattern recognition, e.g. faces, motion videos, speeches. In this paper, first we define pairwise angular similarity and angular distance for linear subspaces. The angular distance satisfies non-negativity, identity of indiscernibles, symmetry and triangle inequality, and thus it is a metric. Then we propose a method to compress linear subspaces into compact similarity-preserving binary signatures, between which the normalized Hamming distance is an unbiased estimator of the angular distance. We provide a lower bound on the length of the binary signatures which suffices to guarantee uniform distance-preservation within a set of subspaces. Experiments on face recognition demonstrate the effectiveness of the binary signature in terms of recognition accuracy, speed and storage requirement. The results show that, compared with the exact method, the approximation with the binary signatures achieves an order of magnitude speed-up, while requiring significantly smaller amount of storage space, yet it still accurately preserves the similarity, and achieves high recognition accuracy comparable to the exact method in face recognition.
【Keywords】: linear subspace;binary signature
【Paper Link】 【Pages】:2773-2779
【Authors】: Richard M. Jiang ; Danny Crookes
【Abstract】: Visual salience is an intriguing phenomenon observed in biological neural systems. Numerous attempts have been made to model visual salience mathematically using various feature contrasts, either locally or globally. However, these algorithmic models tend to ignore the problem’s biological solutions, in which visual salience appears to arise during the propagation of visual stimuli along the visual cortex. In this paper, inspired by the conjecture that salience arises from deep propagation along the visual cortex, we present a Deep Salience model where a multi-layer model based on successive Markov random fields (sMRF) is proposed to analyze the input image successively through its deep belief propagation. As a result, the foreground object can be automatically separated from the background in a fully unsupervised way. Experimental evaluation on the benchmark dataset validated that our Deep Salience model can consistently outperform many state-of-the-art salience models, yielding the higher rates in the precision-recall tests and attaining the better scores in F-measure and mean-square error tests.
【Keywords】: Visual Salience; Markove Random Field; Hierachical Image Analysis; Unsupervised Object Detection
【Paper Link】 【Pages】:2780-2786
【Authors】: Ziheng Jiang ; Ping Guo ; Lihong Peng
【Abstract】: Low-rank coding (LRC), originated from matrix decomposition, is recently introduced into image classification. Following the standard bag-of-words (BOW) pipeline, when coding the data matrix in the sense of low-rankness incorporates contextual information into the traditional BOW model, this can capture the dependency relationship among neighbor patches. It differs from the traditional sparse coding paradigms which encode patches independently. Current LRC-based methods use l_1 norm to increase the discrimination and sparseness of the learned codes. However, such methods fail to consider the local manifold structure between dataspace and dictionary space. To solve this problem, we propose a locality-constrained low-rank coding (LCLR) algorithm for image representations. By using the geometric structure information as a regularization term,we can obtain more discriminative representations. In addition, we present a fast and stable online algorithmto solve the optimization problem. In the experiments,we evaluate LCLR with four benchmarks, including one face recognition dataset (extended Yale B), one handwrittendigit recognition dataset (USPS), and two image datasets (Scene13 for scene recognition and Caltech101 for object recognition). Experimental results show thatour approach outperforms many state-of-the-art algorithmseven with a linear classifier.
【Keywords】: bag-of-words; sparse coding; low-rank coding; locality
【Paper Link】 【Pages】:2787-2795
【Authors】: Xiao-Yuan Jing ; Ruimin Hu ; Fei Wu ; Xi-Lin Chen ; Qian Liu ; Yong-Fang Yao
【Abstract】: Dictionary learning (DL) has now become an important feature learning technique that owns state-of-the-art recognition performance. Due to sparse characteristic of data in real-world applications, DL uses a set of learned dictionary bases to represent the linear decomposition of a data point. Fisher discrimination DL (FDDL) is a representative supervised DL method, which constructs a structured dictionary whose atoms correspond to the class labels. Recent years have witnessed a growing interest in multi-view (more than two views) feature learning techniques. Although some multi-view (or multi-modal) DL methods have been presented, there still exists much room for improvement. How to enhance the total discriminability of dictionaries and reduce their redundancy is a crucial research topic. To boost the performance of multi-view DL technique, we propose an uncorrelated multi-view discrimination DL (UMDDL) approach for recognition. By making dictionary atoms correspond to the class labels such that the obtained reconstruction error is discriminative, UMDDL aims to jointly learn multiple dictionaries with totally favorable discriminative power. Furthermore, we design the uncorrelated constraint for multi-view DL, so as to reduce the redundancy among dictionaries learned from different views. Experiments on several public datasets demonstrate the effectiveness of the proposed approach.
【Keywords】: Dictionary learning (DL); Fisher discrimination DL (FDDL); Uncorrelated multi-view discrimination DL (UMDDL)
【Paper Link】 【Pages】:2796-2802
【Authors】: Evan A. Krause ; Michael Zillich ; Thomas Emrys Williams ; Matthias Scheutz
【Abstract】: Being able to quickly and naturally teach robots new knowledge is critical for many future open-world human-robot interaction scenarios. In this paper we present a novel approach to using natural language context for one-shot learning of visual objects, where the robot is immediately able to recognize the described object. We describe the architectural components and demonstrate the proposed approach on a robotic platform in a proof-of-concept evaluation.
【Keywords】: one shot learning; vision; natural language; robotics
【Paper Link】 【Pages】:2803-2809
【Authors】: Yeqing Li ; Chen Chen ; Wei Liu ; Junzhou Huang
【Abstract】: Recently with the explosive growth of visual content on the Internet, large-scale image search has attracted intensive attention. It has been shown that mapping highdimensional image descriptors to compact binary codes can lead to considerable efficiency gains in both storage and similarity computation of images. However, most existing methods still suffer from expensive training devoted to large-scale binary code learning. To address this issue, we propose a sub-selection based matrix manipulation algorithm which can significantly reduce the computational cost of code learning. As case studies, we apply the sub-selection algorithm to two popular quantization techniques PCA Quantization (PCAQ) and Iterative Quantization (ITQ). Crucially, we can justify the resulting sub-selective quantization by proving its theoretic properties. Extensive experiments are carried out on three image benchmarks with up to one million samples, corroborating the efficacy of the sub-selective quantization method in terms of image retrieval.
【Keywords】: image hashing; image quantization; binary encoding; image search; large-scale; sub-selective
【Paper Link】 【Pages】:2810-2816
【Authors】: Yong Li ; Jing Liu ; Zechao Li ; Yangmuzi Zhang ; Hanqing Lu ; Songde Ma
【Abstract】: Face recognition has been widely studied due to its importance in various applications. However, the case that both training images and testing images are corrupted is not well addressed. Motivated by the success of low-rank matrix recovery, we propose a novel semi-supervised low-rank matrix recovery algorithm for robust face recognition. The proposed method can learn robust discriminative representations for both training images and testing images simultaneously by exploiting the classwise block-diagonal structure. Specifically, low-rank matrix approximation can handle the possible contamination of data. Moreover, the classwise block-diagonal structure is exploited to promote discrimination of representations for robust recognition. The above issues are formulated into a unified objective function and we design an efficient optimization procedure based on augmented Lagrange multiplier method to solve it. Extensive experiments on three public databases are performed to validate the effectiveness of our approach. The strong identification capability of representations with block-diagonal structure is verified.
【Keywords】: Low-Rank Representations； Classwise Block-Diagonal Structure；Robust Face Recognition
【Paper Link】 【Pages】:2817-2823
【Authors】: Matthai Philipose
【Abstract】: We examine how to use emerging far-infrared imager ensembles to detect certain objects of interest (e.g., faces, hands, people and animals) in synchronized RGB video streams at very low power. We formulate the problem as one of selecting subsets of sensing elements (among many thousand possibilities) from the ensembles for tests. The subset selection problem is naturally adaptive and online: testing certain elements early can obviate the need for testing many others later, and selection policies must be updated at inference time. We pose the ensemble sensor selection problem as a structured extension of test-cost-sensitive classification, propose a principled suite of techniques to exploit ensemble structure to speed up processing and show how to re-estimate policies fast. We estimate reductions in power consumption of roughly 50x relative to even highly optimized implementations of face detection, a canonical object-detection problem. We also illustrate the benefits of adaptivity and online estimation.
【Keywords】: computer vision; low-power inference; cost-sensitive inference
【Paper Link】 【Pages】:2824-2830
【Authors】: Joseph Roth ; Xiaoming Liu
【Abstract】: We present an algorithm for identity verification using only information from the hair. Face recognition in the wild (i.e., unconstrained settings) is highly useful in a variety of applications, but performance suffers due to many factors, e.g., obscured face, lighting variation, extreme pose angle, and expression. It is well known that humans utilize hair for identification under many of these scenarios due to either the consistent hair appearance of the same subject or obvious hair discrepancy of different subjects, but little work exists to replicate this intelligence artificially. We propose a learned hair matcher using shape, color, and texture features derived from localized patches through an AdaBoost technique with abstaining weak classifiers when features are not present in the given location. The proposed hair matcher achieves 71.53% accuracy on the LFW View 2 dataset. Hair also reduces the error of a Commercial Off-The-Shelf (COTS) face matcher through simple score-level fusion by 5.7%.
【Keywords】: Hair; Face Recognition;
【Paper Link】 【Pages】:2831-2838
【Authors】: Min Joon Seo ; Hannaneh Hajishirzi ; Ali Farhadi ; Oren Etzioni
【Abstract】: Automatically solving geometry questions is a long-standing AI problem. A geometry question typically includes a textual description accompanied by a diagram. The first step in solving geometry questions is diagram understanding, which consists of identifying visual elements in the diagram, their locations, their geometric properties, and aligning them to corresponding textual descriptions. In this paper, we present a method for diagram understanding that identifies visual elements in a diagram while maximizing agreement between textual and visual data. We show that the method's objective function is submodular; thus we are able to introduce an efficient method for diagram understanding that is close to optimal. To empirically evaluate our method, we compile a new dataset of geometry questions (textual descriptions and diagrams) and compare with baselines that utilize standard vision techniques. Our experimental evaluation shows an F1 boost of more than 17% in identifying visual elements and 25% in aligning visual elements with their textual descriptions.
【Keywords】: artificial intelligence; AI; diagram understanding; geometry problem; math problem; hough; submodular; diagram representation
【Paper Link】 【Pages】:2839-2845
【Authors】: Dror Sholomon ; Omid E. David ; Nathan S. Netanyahu
【Abstract】: In this paper we introduce new types of square-piece jigsaw puzzles, where in addition to the unknown location and orientation of each piece, a piece might also need to be flipped. These puzzles, which are associated with a number of real world problems, are considerably harder, from a computational standpoint. Specifically, we present a novel generalized genetic algorithm (GA)-based solver that can handle puzzle pieces of unknown location and orientation (Type 2 puzzles) and (two-sided) puzzle pieces of unknown location, orientation, and face (Type 4 puzzles). To the best of our knowledge, our solver provides a new state-of-the-art, solving previously attempted puzzles faster and far more accurately, handling puzzle sizes that have never been attempted before, and assembling the newly introduced two-sided puzzles automatically and effectively. This paper also presents, among other results, the most extensive set of experimental results, compiled as of yet, on Type 2 puzzles.
【Keywords】: Jigsaw Puzzle; Genetic Algorithms
【Paper Link】 【Pages】:2846-2852
【Authors】: Hua Wang ; Feiping Nie ; Heng Huang
【Abstract】: Video completion is a computer vision technique to recover the missing values in video sequences by filling the unknown regions with the known information. In recent research, tensor completion, a generalization of matrix completion for higher order data, emerges as a new solution to estimate the missing information in video with the assumption that the video frames are homogenous and correlated. However, each video clip often stores the heterogeneous episodes and the correlations among all video frames are not high. Thus, the regular tenor completion methods are not suitable to recover the video missing values in practical applications. To solve this problem, we propose a novel spatially-temporally consistent tensor completion method for recovering the video missing data. Instead of minimizing the average of the trace norms of all matrices unfolded along each mode of a tensor data, we introduce a new smoothness regularization along video time direction to utilize the temporal information between consecutive video frames. Meanwhile, we also minimize the trace norm of each individual video frame to employ the spatial correlations among pixels. Different to previous tensor completion approaches, our new method can keep the spatio-temporal consistency in video and do not assume the global correlation in video frames. Thus, the proposed method can be applied to the general and practical video completion applications. Our method shows promising results in all evaluations on both 3D biomedical image sequence and video benchmark data sets.
【Keywords】: Tensor Completion; Video Completion
【Paper Link】 【Pages】:2853-2859
【Authors】: Wenxuan Xie ; Yuxin Peng ; Jianguo Xiao
【Abstract】: We investigate weakly-supervised image parsing, i.e., assigning class labels to image regions by using image-level labels only. Existing studies pay main attention to the formulation of the weakly-supervised learning problem, i.e., how to propagate class labels from images to regions given an affinity graph of regions. Notably, however, the affinity graph of regions, which is generally constructed in relatively simpler settings in existing methods, is of crucial importance to the parsing performance due to the fact that the weakly-supervised parsing problem cannot be solved within a single image, and that the affinity graph enables label propagation among multiple images. In order to embed more semantics into the affinity graph, we propose novel criteria by exploiting the weak supervision information carefully, and develop two graphs: L1 semantic graph and k-NN semantic graph. Experimental results demonstrate that the proposed semantic graphs not only capture more semantic relevance, but also perform significantly better than conventional graphs in image parsing.
【Keywords】:
【Paper Link】 【Pages】:2860-2866
【Authors】: Caiming Xiong ; Scott McCloskey ; Shao-Hang Hsieh ; Jason J. Corso
【Abstract】: To improve robustness to significant mismatches between source domain and target domain - arising from changes such as illumination, pose and image quality - domain adaptation is increasingly popular in computer vision. But most of methods assume that the source data is from single domain, or that multi-domain datasets provide the domain label for training instances. In practice, most datasets are mixtures of multiple latent domains, and difficult to manually provide the domain label of each data point. In this paper, we propose a model that automatically discovers latent domains in visual datasets. We first assume the visual images are sampled from multiple manifolds, each of which represents different domain, and which are represented by different subspaces. Using the neighborhood structure estimated from images belonging to the same category, we approximate the local linear invariant subspace for each image based on its local structure, eliminating the category-specific elements of the feature. Based on the effectiveness of this representation, we then propose a squared-loss mutual information based clustering model with category distribution prior in each domain to infer the domain assignment for images. In experiment, we test our approach on two common image datasets, the results show that our method outperforms the existing state-of-the-art methods, and also show the superiority of multiple latent domain discovery.
【Keywords】: Latent Domain Discovery; Clustering; Domain Adaptation
【Paper Link】 【Pages】:2867-2873
【Authors】: Ke Zhang ; Wei Zhang ; Sheng Zeng ; Xiangyang Xue
【Abstract】: In this paper we propose a novel method for image semantic segmentation using multiple graphs. The multiview affinity graph is constructed by leveraging the consistency between semantic space and multiple visualspaces. With block-diagonal constraints, we enforce the affinity matrix to be sparse such that the pairwise potential for dissimilar superpixels is close to zero. By a divide-and-conquer strategy, the optimizationfor learning affinity matrix is decomposed into several subproblems that can be solved in parallel. Using the neighborhood relationship between superpixels and the consistency between affinity matrix and labelconfidencematrix, we infer the semantic label for each superpixel of unlabeled images by minimizing an objective whose closed form solution can be easily obtained. Experimental results on two real-world image datasetsdemonstrate the effectiveness of our method.
【Keywords】: Semantic Segmentation;Multiple Feature Fusion; Semi-Supervised Learning
【Paper Link】 【Pages】:2874-2881
【Authors】: Kang Zhao ; Hongtao Lu ; Jincheng Mei
【Abstract】: Hashing has recently attracted considerable attention for large scale similarity search. However, learning compact codes with good performance is still a challenge. In many cases, the real-world data lies on a low-dimensional manifold embedded in high-dimensional ambient space. To capture meaningful neighbors, a compact hashing representation should be able to uncover the intrinsic geometric structure of the manifold, e.g., the neighborhood relationships between subregions. Most existing hashing methods only consider this issue during mapping data points into certain projected dimensions. When getting the binary codes, they either directly quantize the projected values with a threshold, or use an orthogonal matrix to refine the initial projection matrix, which both consider projection and quantization separately, and will not well preserve the locality structure in the whole learning process. In this paper, we propose a novel hashing algorithm called Locality Preserving Hashing to effectively solve the above problems. Specifically, we learn a set of locality preserving projections with a joint optimization framework, which minimizes the average projection distance and quantization loss simultaneously. Experimental comparisons with other state-of-the-art methods on two large scale datasets demonstrate the effectiveness and efficiency of our method.
【Keywords】: Similarity Search;Binary Codes;Locality Preserving Hashing
【Paper Link】 【Pages】:2882-2889
【Authors】: Tim Chan ; Joseph I ; Carlos Macasaet ; Daniel Kang ; Robert M. Hardy ; Carlos Ruiz ; Rigel Porras ; Brian Baron ; Karim Qazi ; Padraic Hannon ; Tomonori Honda
【Abstract】: This paper will demonstrate a machine learning appli- cation for predicting positive lead conversion events on the Edmunds.com website, an American destination for car shopping. A positive conversion event occurs when a user fills out and submits a lead form interstitial. We used machine learning to identify which users might want to fill out lead forms, and where in their sessions to present the interstitials. There are several factors that make these predictions difficult, such as (a) far more negative than positive responses (b) seasonality effects due to car sales events near holidays, which require the model to be easily tunable and (c) the need for compu- tationally fast predictions for real-time decision-making in order to minimize any impact on the website’s us- ability. Rather than develop a single highly complex model, we used an ensemble of three simple models: Naive Bayes, Markov Chain, and Vowpal Wabbit. The ensemble generated significant lift over random predic- tions and demonstrated comparable accuracy to an ex- ternal consulting company’s model.
【Keywords】: naive-bayes;markov-chain;vowpal-wabbit;prediction;automotive;lead-form;lead-conversion
【Paper Link】 【Pages】:2890-2897
【Authors】: Andy Hon Wai Chun ; Ted Yiu Tat Suen
【Abstract】: This paper describes how AI is used to plan, schedule, and optimize nightly engineering works for both the commuter and rapid transit lines in Hong Kong. The MTR Corporation Limited operates and manages all the rail lines in Hong Kong. Its “Engineering Works and Traffic Information Management System” (ETMS) is a mission critical system that manages all information related to engineering works and their related track possessions and engineering train movements. The AI Engine described in this paper is a component of this ETMS. In Hong Kong, the maintenance, inspection, repair, or installation works along the rail lines are done during the very short non-traffic hours (NTH) of roughly 4 to 5 hours each night. These engineering works can be along the running tracks, track-side, tunnel, freight yards, sub-depots, depot maintenance tracks, etc. The proper scheduling of necessary engineering works is crucial to maintaining a reliable and safe train service during normal hours. The AI Engine optimizes resource allocation to maximize the number of engineering works that can be performed, while ensuring all safety, environment, and operational rules and constraints are met. The work described is part of a project to redesign and replace the existing ETMS, deployed in 2004, with an updated technology platform and modern IT architecture, to provide a more robust and scalable system that potentially can be deployed to other cities around the world.
【Keywords】: scheduling; optimization; rules; railway
【Paper Link】 【Pages】:2898-2905
【Authors】: Randall Davis ; David J. Libon ; Rhoda Au ; David Pitman ; Dana L. Penney
【Abstract】: The Digital Clock Drawing Test is a fielded application that provides a major advance over existing neuropsychological testing technology. It captures and analyzes high precision information about both outcome and process, opening up the possibility of detecting subtle cognitive impairment even when test results appear superficially normal. We describe the design and development of the test, document the role of AI in its capabilities, and report on its use over the past seven years. We outline its potential implications for earlier detection and treatment of neurological disorders. We also set the work in the larger context of the THink project, which is exploring multiple approaches to determining cognitive status through the detection and analysis of subtle behaviors.
【Keywords】: digital clock drawing test; cognitive status
【Paper Link】 【Pages】:2906-2913
【Authors】: Richard Hoshino ; Caleb Raible-Clark
【Abstract】: Course allocation is one of the most complex issues facing any university, due to the sensitive nature of deciding which subset of students should be granted seats in highly-popular (market-scarce) courses. In recent years, researchers have proposed numerous solutions, using techniques in integer programming, combinatorial auction design, and matching theory. In this paper, we present a four-part AI-based course allocation algorithm that was conceived by an undergraduate student, and recently implemented at a small Canadian liberal arts university. This new allocation process, which builds upon the Harvard Business School Draft, has received overwhelming support from students and faculty for its transparency, impartiality, and effectiveness.
【Keywords】: course allocation; multi-unit assignment problem; proxy algorithms
【Paper Link】 【Pages】:2914-2921
【Authors】: Alfred Krzywicki ; Wayne Wobcke ; Yang Sok Kim ; Xiongcai Cai ; Michael Bain ; Paul Compton ; Ashesh Mahidadia
【Abstract】: This paper reports on the successful deployment of a people-to-people recommender system in a large commercial online dating site. The deployment was the result of thorough evaluation and an online trial of a number of methods, including profile-based, collaborative filtering and hybrid algorithms. Results taken a few months after deployment show that key metrics generally hold their value or show an increase compared to the trial results, and that the recommender system delivered its projected benefits.
【Keywords】: Recommender Systems, Data Mining
【Paper Link】 【Pages】:2922-2929
【Authors】: Wei Li ; Justin Matejka ; Tovi Grossman ; George W. Fitzmaurice
【Abstract】: In 2009 we presented the idea of using collaborative filtering within a complex software application to help users learn new and relevant commands (Matejka et al. 2009). This project continued to evolve and we explored the design space of a contextual software command recommender system and completed a four-week user study (Li et al. 2011). We then expanded the scope of our project by implementing CommunityCommands, a fully functional and deployable recommender system. CommunityCommands was made available as a publically available plug-in download for Autodesk’s flagship software application AutoCAD. During a one-year period, the recommender system was used by more than 1100 AutoCAD users. In this paper, we present our system usage data and payoff. We also provide an in-depth discussion of the challenges and design issues associated with developing and deploying the front end AutoCAD plug-in and its back end system. This includes a detailed description of the issues surrounding cold start and privacy. We also discuss how our practical system architecture was designed to leverage Autodesk’s existing Customer Involvement Program (CIP) data to deliver in-product contextual recommendations to end-users. Our work sets important groundwork for the future development of recommender systems within the domain of end-user software learning assistance.
【Keywords】: Recommender system; CAD; Learning; privacy
【Paper Link】 【Pages】:2930-2937
【Authors】: Jian Wu ; Kyle Williams ; Hung-Hsuan Chen ; Madian Khabsa ; Cornelia Caragea ; Alexander Ororbia ; Douglas Jordan ; C. Lee Giles
【Abstract】: CiteSeerX is a digital library search engine that provides access to more than 4 million academic documents with nearly a million users and millions of hits per day. Artificial intelligence (AI) technologies are used in many components of CiteSeerX, e.g. to accurately extract metadata, intelligently crawl the web, and ingest documents. We present key AI technologies used in the following components: document classification and deduplication, document and citation clustering, automatic metadata extraction and indexing, and author disambiguation. These AI technologies have been developed by CiteSeerX group members over the past 5–6 years. We also show the usage status, payoff, development challenges, main design concepts, and deployment and maintenance requirements. While it is challenging to rebuild a system like CiteSeerX from scratch, many of these AI technologies are transferable to other digital libraries and/or search engines.
【Keywords】: CiteSeerX; digital library; machine learning; document classification; random forests; support vector machine; conditional random fields; DBSCAN; logistic regression; naive Bayesian; decision trees
【Paper Link】 【Pages】:2938-2945
【Authors】: Hazem Radwan Ahmed ; Janice I. Glasgow
【Abstract】: Pattern discovery in protein interaction networks can reveal crucial biological knowledge on the inner workings of cellular machinery. Although far from complete, extracting meaningful patterns from proteomic networks is a non-trivial task due to their size-complexity. This paper proposes a computational framework to efficiently discover topologically-similar patterns from large proteomic networks using Particle Swarm Optimization (PSO). PSO is a robust and low-cost optimization technique that demonstrated to work effectively on the complex, mostly sparse proteomic networks. The resulting topologically-similar patterns of close proximity are utilized to systematically predict new high-confidence protein-protein interactions (PPIs). The proposed PSO-based PPI prediction method (3PI) managed to predict high-confidence PPIs, validated by more than one computational/experimental source, through a proposed PPI knowledge transfer process between topologically-similar interaction patterns of close proximity. In three case studies, over 50% of the predicted interactions for EFGR, ERBB2, ERBB3, GRB2 and UBC are overlapped with publically available interaction databases, ~80% of the predictions are found among the Top 1% results of another PPI prediction method and their genes are significantly co-expressed across different tissues. Moreover, the only single prediction example that did not overlap with any of our validation sources was recently experimentally supported by two PubMed publications.
【Keywords】: Multi-Start Particle Swarm Optimization; Pattern Discovery; Proteomic Networks; Protein Interaction Prediction.
【Paper Link】 【Pages】:2946-2953
【Authors】: Bahadir Ismail Aydin ; Yavuz Selim Yilmaz ; Yaliang Li ; Qi Li ; Jing Gao ; Murat Demirbas
【Abstract】: We leverage crowd wisdom for multiple-choice question answering, and employ lightweight machine learning techniques to improve the aggregation accuracy of crowdsourced answers to these questions. In order to develop more effective aggregation methods and evaluate them empirically, we developed and deployed a crowdsourced system for playing the "Who wants to be a millionaire?" quiz show.Analyzing our data (which consist of more than 200,000 answers), we find that by just going with the most selected answer in the aggregation, we can answer over 90% of the questions correctly, but the success rate of this technique plunges to 60% for the later/harder questions in the quiz show. To improve the success rates of these later/harder questions, we investigate novel weighted aggregation schemes for aggregating the answers obtained from the crowd.By using weights optimized for reliability of participants (derived from the participants' confidence), we show that we can pull up the accuracy rate for the harder questions by 15%, and to overall 95% average accuracy.Our results provide a good case for the benefits of applying machine learning techniques for building more accurate crowdsourced question answering systems.
【Keywords】: Crowdsourcing; Multiple-choice question answering; AI for games
【Paper Link】 【Pages】:2954-2959
【Authors】: Amos Azaria ; Sarit Kraus ; Claudia V. Goldman ; Omer Tsimhoni
【Abstract】: Reducing energy consumption of climate control systems is important in order to reduce human environmental footprint. The need to save energy becomes even greater when considering an electric car, since heavy use of the climate control system may exhaust the battery. In this paper we consider a method for an automated agent to provide advice to drivers which will motivate them to reduce the energy consumption of their climate control unit. Our approach takes into account both the energy consumption of the climate control system and the expected comfort level of the driver. We therefore build two models, one for assessing the energy consumption of the climate control system as a function of the system's settings, and the other, models human comfort level as a function of the climate control system's settings. Using these models, the agent provides advice to the driver considering how to set the climate control system. The agent advises settings which try to preserve a high level of comfort while consuming as little energy as possible. We empirically show that drivers equipped with our agent which provides them with advice significantly save energy as compared to drivers not equipped with our agent.
【Keywords】: Human modeling; advice provision; persuasion
【Paper Link】 【Pages】:2960-2965
【Authors】: Bruno Bouchard ; Kevin Bouchard ; Abdenour Bouzouane
【Abstract】: People suffering from a loss of autonomy caused by a cognitive deficit generally have to perform important daily tasks (such as cooking) using devices and appliances designed for healthy people, which do not take into consideration their cognitive impairment. Using these devices is risky and may lead to a tragedy (e.g. fire). A potential solution to this issue is to provide automated systems, which perform tasks on behalf of the patient. However, clinical studies have shown that encouraging users to maintain their autonomy greatly help to preserve health, dignity, and motivation. Therefore, we present in this paper a new smart range prototype allowing monitoring and guiding a cognitively-impaired user in the activity of preparing a meal. This new original prototype is capable of giving adapted prompting to the user in the completion of several recipes by exploiting load cells, heat sensors and electromagnetic contacts embedded in the range. We currently own a provisional patent on this new invention, and we completed a first experimental phase.
【Keywords】: Smart range; Load cells; State-transition; Cognitive impairment
【Paper Link】 【Pages】:2966-2971
【Authors】: Matthew Brown ; Sandhya Saisubramanian ; Pradeep Varakantham ; Milind Tambe
【Abstract】: To dissuade reckless driving and mitigate accidents, cities deploy resources to patrol roads. In this paper, we present STREETS, an application developed for the city of Singapore, which models the problem of computing randomized traffic patrol strategies as a defender-attacker Stackelberg game. Previous work on Stackelberg security games has focused extensively on counter-terrorism settings. STREETS moves beyond counter-terrorism and represents the first use of Stackelberg games for traffic patrolling, in the process providing a novel algorithm for solving such games that addresses three major challenges in modeling and scale-up. First, there exists a high degree of unpredictability in travel times through road networks, which we capture using a Markov Decision Process for planning the patrols of the defender (the police) in the game. Second, modeling all possible police patrols and their interactions with a large number of adversaries (drivers) introduces a significant scalability challenge. To address this challenge we apply a compact game representation in a novel fashion combined with adversary and state sampling. Third, patrol strategies must balance exploitation (minimizing violations) with exploration (maximizing omnipresence), a tradeoff we model by solving a bi-objective optimization problem. We present experimental results using real-world traffic data from Singapore. This work is done in collaboration with the Singapore Ministry of Home Affairs and is currently being evaluated by the Singapore Police Force.
【Keywords】: game theory; multi-agent systems; planning; markov decision processes; security
【Paper Link】 【Pages】:2972-2977
【Authors】: Florent Garcin ; Boi Faltings
【Abstract】: There is much interest in crowdsourcing information that is distributed among many individuals, such as the likelihood of future events, election outcomes, the quality of products, or the consequence of a decision. To obtain accurate outcomes, various game-theoretic incentive schemes have been proposed. However, only prediction markets have been tried in practice. In this paper, we describe an experimental platform, swissnoise, that compares prediction markets with peer prediction schemes developed in recent AI research. It shows that peer prediction schemes can achieve similar performance while being applicable to a much broader range of questions.
【Keywords】: crowdsourcing; prediction market; peer prediction, experimental platform, user study
【Paper Link】 【Pages】:2978-2983
【Authors】: William B. Haskell ; Debarun Kar ; Fei Fang ; Milind Tambe ; Sam Cheung ; Elizabeth Denicola
【Abstract】: Fish stocks around the world are in danger from ille- gal fishing. In collaboration with the U.S. Coast Guard (USCG), we work to defend fisheries from illegal fish- erman (henceforth called Lanchas) in the U.S. Gulf of Mexico. We have developed the COmPASS (Conserva- tive Online Patrol ASSistant) system to design USCG patrols against the Lanchas. In this application, we face a population of Lanchas with heterogeneous behavior who fish frequently. We have some data about these Lanchas, but not enough to fit a statistical model. Previ- ous security patrol assistants have focused on counter- terrorism in one-shot games where adversaries are as- sumed to be perfectly rational, and much less data about their behavior is available. COmPASS is novel because: (i) it emphasizes environmental crime; (ii) it is based on a repeated Stackelberg game; (iii) it allows for bounded rationality of the Lanchas and it offers a robust approach against the heterogeneity of the Lancha population; and (iv) it can learn from sparse Lancha data. We report the effectiveness of COmPASS in the Gulf in our numeri- cal experiments based on real fish data. The COmPASS system is to be tested by USCG.
【Keywords】:
【Paper Link】 【Pages】:2984-2989
【Authors】: Noel Hollingsworth ; Jason Meyer ; Ryan McGee ; Jeffrey Doering ; George Konidaris ; Leslie Pack Kaelbling
【Abstract】: We applied a policy search algorithm to the problem of optimizing a start-stop controller — a controller used in a car to turn off the vehicle's engine, and thus save energy, when the vehicle comes to a temporary halt. We were able to improve the existing policy by approximately 12% using real driver trace data. We also experimented with using multiple policies, and found that doing so could lead to a further 8% improvement if we could determine which policy to apply at each stop. The driver's behaviors before stopping were found to be uncorrelated with the policy that performed best; however, further experimentation showed that the driver's behavior during the stop may be more useful, suggesting a useful direction for adding complexity to the underlying start-stop policy.
【Keywords】: Machine learning; Reinforcement learning; Automative Applications
【Paper Link】 【Pages】:2990-2997
【Authors】: GeunSik Jo ; Kyeong-Jin Oh ; Inay Ha ; Kee-Sung Lee ; Myung-Duk Hong ; Ulrich Neumann ; Suya You
【Abstract】: Aircraft maintenance and training play one of the most important roles in ensuring flight safety. The maintenance process usually involves massive numbers of components and substantial procedural knowledge of maintenance procedures. Maintenance tasks require technicians to follow rigorous procedures to prevent operational errors in the maintenance process. In addition, the maintenance time is a cost-sensitive issue for airlines. This paper proposes intelligent augmented reality (IAR) system to minimize operation errors and time-related costs and help aircraft technicians cope with complex tasks by using an intuitive UI/UX interface for their maintenance tasks. The IAR system is composed mainly of three major modules: 1) the AR module 2) the knowledge-based system (KBS) module 3) a unified platform with an integrated UI/UX module between the AR and KBS modules. The AR module addresses vision-based tracking, annotation, and recognition. The KBS module deals with ontology-based resources and context management. Overall testing of the IAR system is conducted at Korea Air Lines (KAL) hangars. Tasks involving the removal and installation of pitch trimmers in landing gear are selected for benchmarking purposes, and according to the results, the proposed IAR system can help technicians to be more effective and accurate in performing their maintenance tasks.
【Keywords】: Intelligent Augmented Reality System; Augmented Reality; Knowledge Based-System; Aircraft Maintenance; Unified Framework for AR and Knowledge-based Systems
【Paper Link】 【Pages】:2998-3003
【Authors】: Jeremy Ludwig ; Annaka Kalton ; Robert Richards ; Brian Bautsch ; Craig Markusic ; J. Schumacher
【Abstract】: Whenever an auto manufacturer refreshes an existing car or truck model or builds a new one, the model will undergo hundreds if not thousands of tests before the factory line and tooling is finished and vehicle production beings. These tests are generally carried out on expensive, custom-made vehicles because the new factory lines for the model do not exist yet. The work presented in this paper describes how an existing intelligent scheduling software framework was modified to include domain-specific heuristics used in the vehicle test planning process. The result of this work is a prototype scheduling tool that optimizes the overall given test schedule in order to complete the work in a given time window while minimizing the total number of vehicles required for the test schedule. Initial results are presented that show a reduction in required test vehicles compared to manual scheduling of the same tasks as well as increased capability to ask “what-if” questions to further improve the schedule.
【Keywords】:
【Paper Link】 【Pages】:3004-3009
【Authors】: Lydia Manikonda ; Tathagata Chakraborti ; Sushovan De ; Kartik Talamadupula ; Subbarao Kambhampati
【Abstract】: One subclass of human computation applications are those directed at tasks that involve planning (e.g. tour planning) and scheduling (e.g. conference scheduling). Interestingly, work on these systems shows that even primitive forms of automated oversight on the human contributors helps in significantly improving the effectiveness of the humans/crowd. In this paper, we argue that the automated oversight used in these systems can be viewed as a primitive automated planner, and that there are several opportunities for more sophisticated automated planning in effectively steering the crowd. Straightforward adaptation of current planning technology is however hampered by the mismatch between the capabilities of human workers and automated planners. We identify and partially address two important challenges that need to be overcome before such adaptation of planning technology can occur: (1 interpreting inputs of the human workers (and the requester) and (2) steering or critiquing plans produced by the human workers, armed only with incomplete domain and preference models. To these ends, we describe the implementation of AI-MIX, a tour plan generation system that uses automated checks and alerts to improve the quality of plans created by human workers; and present a preliminary evaluation of the effectiveness of steering provided by automated planning.
【Keywords】: Automated Planning; Crowdsourcing; Mturk
【Paper Link】 【Pages】:3010-3016
【Authors】: Peter Z. Yeh ; Benjamin Douglas ; William Jarrold ; Adwait Ratnaparkhi ; Deepak Ramachandran ; Peter F. Patel-Schneider ; Stephen Laverty ; Nirvana Tikku ; Sean Brown ; Jeremy Mendel
【Abstract】: In this paper, we present a speech-driven second screen application for TV program discovery. We give an overview of the application and its architecture. We also present a user study along with a failure analysis. The results from the study are encouraging, and demonstrate our application's effectiveness in the target domain. We conclude with a discussion of follow-on efforts to further enhance our application.
【Keywords】: Knowledge-Based System; NLIDB; Ontology; Natural Language Processing; Speech-Driven Search; TV Program Discovery
【Paper Link】 【Pages】:3017-3023
【Authors】: Jun Yu ; Weng-Keen Wong ; Steve Kelling
【Abstract】: Although citizen science projects such as eBird can compile large volumes of data over broad spatial and temporal extents, the quality of this data is a concern due to differences in the skills of volunteers at identifying bird species. Species accumulation curves, which plot the number of unique species observed over time, are an effective way to quantify the skill level of an eBird participant. Intuitively, more skilled observers can identify a greater number of species per unit time than inexperienced birders, resulting in a steeper curve. We propose a mixture model for clustering species accumulation curves. These clusters enable the identification of distinct skill levels of eBird participants, which can then be used to build more accurate species distribution models and to develop automated data quality filters.
【Keywords】: Citizen Science; Crowdsourcing; Species Accumulation Curves
【Paper Link】 【Pages】:3024-3030
【Authors】: Alfred Zong ; Yuke Zhu
【Abstract】: Machine learning techniques have been successfully applied to Chinese character recognition; nonetheless, automatic generation of stylized Chinese handwriting remains a challenge. In this paper, we propose StrokeBank, a novel approach to automating personalized Chinese handwriting generation. We use a semi-supervised algorithm to construct a dictionary of component mappings from a small seeding set. Unlike previous work, our approach does not require human supervision in stroke extraction or knowledge of the structure of Chinese characters. This dictionary is used to generate handwriting that preserves stylistic variations, including cursiveness and spatial layout of strokes. We demonstrate the effectiveness of our model by a survey-based evaluation. The results show that our generated characters are nearly indistinguishable from ground truth handwritings.
【Keywords】: semi-supervised learning; handwriting generation; personalization; pattern recognition; art and music
【Paper Link】 【Pages】:3031-3036
【Authors】: Emilie Featherston ; Mohan Sridharan ; Susan Darling Urban ; Joseph E. Urban
【Abstract】: Dorothy is an integrated 3D/robotics educational tool created by augmenting the Alice programming environment for teaching core computing skills to students without prior programming experience. The tool provides a drag and drop interface to create graphical routines in virtual worlds; these routines are automatically translated into code to provide a real-time or offline enactment on mobile robots in the real world. This paper summarizes the key capabilities of Dorothy, and describes the contributions made to: (a) enhance the bidirectional communication between the virtual interface and robots; and (b) support multirobot collaboration. Specifically, we describe the ability to automatically revise the virtual world based on sensor data obtained from robots, creating or deleting objects in the virtual world based on their observed presence or absence in the real world. Furthermore, we describe the use of visually observed behavior of teammates for collaboration between robots when they cannot communicate with each other. Dorothy thus helps illustrate sophisticated algorithms for fundamental challenges in robotics and AI to teach advanced computing concepts, and to emphasize the importance of computing in real world applications, to beginning programmers.
【Keywords】: Educational tool; Robotics; Artificial intelligence; Computational thinking
【Paper Link】 【Pages】:3037-3049
【Authors】: Robert Selkowitz ; Debra T. Burhans
【Abstract】: We report on Shallow Blue (SB), an autonomous chess agent constructed by a small group of faculty and undergraduate students at Canisius College. In addition to pushing the limits of consumer grade components at low cost, SB is a focal point for interdisciplinary student projects spanning computer science, engineering, and physics. We demonstrate that undergraduate students can engage in rich, long-term robotic design and applied Artificial Intelligence (AI) from both hardware and software perspectives. Student outcomes of SB include senior theses, conference presentations, peer-reviewed publications, and admission to graduate programs. Students who participated also report substantial development in skills and knowledge applicable to their post-undergraduate education and careers.
【Keywords】: embodied AI, education
【Paper Link】 【Pages】:3050-3051
【Authors】: Michael Wollowski
【Abstract】: In this paper, we describe how we integrated the materials from the 2013 IBM The Great Minds Challenge (TGMC) - Watson Technical Edition into our Introductory Artificial Intelligence course. We describe the variety of materials made available by IBM, as well as the nature of the competition and the datasets that are at the heart of it. We detail how, where and in what form we integrated the materials into our course. We describe assignments that are based on the materials from the competition as well as additional materials we incorporated into our course. We finish by evaluating our experience in teaching with the materials as well as summarize relevant student feedback. We make recommendations for those who wish to adopt the materials.
【Keywords】: Introductory Artificial Intelligence course; IBM Watson; The Great Minds Challenge - Watson Technical Edition
【Paper Link】 【Pages】:3052-3054
【Authors】: Robert Selkowitz ; Michael Heilemann ; Jon Mrowczynski
【Abstract】: We report on Jim, an inexpensive student designed platform for embodied affective AI. The project brings together students from backgrounds in computer science, physics, engineering, and Digital Media Arts (DMA) in an informal educational setting. The platform will be used in AI courses and autism treatment studies.
【Keywords】: embodied AI, affective robotics, education
【Paper Link】 【Pages】:3055-
【Authors】: Kartik Talamadupula ; Subbarao Kambhampati
【Abstract】: One of the more important aims of graduate artificial intelligence courses is to prepare graduate students to critically evaluate the current literature. The established approaches for this include either asking a student to present a paper in class, or to have the entire class read and discuss a paper. However, neither of these approaches presents incentives for student participation beyond the posting of a single summary or review. In this paper, we describe a class project that uses the popular Easychair conference management system as a pedagogical tool to enable engagement in the peer review process. We report on the deployment of this project in a medium-sized graduate AI class, and present the results of this deployment. We hope that the success of this project in engaging students in the peer review process can be used better train and bolster the future corps of AI reviewers.
【Keywords】: project, artificial intelligence, peer review, reviewing
【Paper Link】 【Pages】:3054-3056
【Authors】: Todd W. Neller ; Laura E. Brown ; Roger L. West ; James E. Heliotis ; Sean Strout ; Ivona Bezáková ; Bikramjit Banerjee ; Daniel Lucas Thompson
【Abstract】: The Model AI Assignments session seeks to gather and disseminate the best assignment designs of the Artificial Intelligence (AI) Education community. Recognizing that assignments form the core of student learning experience, we here present abstracts of five AI assignments from the 2014 session that are easily adoptable, playfully engaging, and flexible for a variety of instructor needs. Assignment specifications and supporting resources may be found at modelai.gettysburg.edu.
【Keywords】:
【Paper Link】 【Pages】:3057-3058
【Authors】: Thomas E. Allen
【Abstract】: Conditional preference networks (CP-nets) exploit the power of conditional ceteris paribus rules to enable a compact representation of human preferences. CP-nets have much appeal. However, the study of CP-nets has not advanced sufficiently for their widespread use in complex, real-world applications. Most studies limit their attention to strict, complete, consistent preferences over binary domains. In my research, I attempt to address these limitations to make CP-nets more useful. I discuss recent research in which we presented a novel algorithm for learning CP-nets from user queries, as well as work showing how to adapt existing algorithms to learn and reason with multivalued CP-nets that can model indifference as well as strict preference. I outline anticipated research to extend our elicitation algorithm to a richer class of CP-nets, develop a formal model of expected flipping sequence length, and learn CP-nets in which a subject prefers to coordinate certain features, leading to a cycle in the dependency graph.
【Keywords】: CP-nets; indifference; multivalued features; incomplete preferences
【Paper Link】 【Pages】:3059-3060
【Authors】: Ofra Amir
【Abstract】: Teamwork and care coordination are of increasing importance to health care delivery and patient safety and health. My research aims at developing agents that are able to make intelligent information sharing decisions to support a diverse, evolving team of care providers in constructing and maintaining a shared plan that operates in uncertain environments.
【Keywords】: multi-agent systems;information sharing
【Paper Link】 【Pages】:3061-3062
【Authors】: Jason M. Bindewald
【Abstract】: One of the defining characteristics of an adaptive automation system is the hand-off from machine to human--and vice versa. This research seeks to improve system control hand-offs, by investigating how the manner in which the automation completes its task affects the overall performance of the human-machine team. Specifically, the research will explore how the level of similarity of action choices between the automation and the human operator affects the resulting system's performance. A design process model for creating adaptive automation systems is complete, and was used to design an adaptive automation research environment. Data gathered using this system will be used to automate user task performance in the system, and allow for research into the effects of that automation.
【Keywords】: Adaptive Automation; Human-Computer Interaction; Player Profiling
【Paper Link】 【Pages】:3063-3064
【Authors】: Adrian Boteanu
【Abstract】: This thesis seeks to address word reasoning problems from a semantic standpoint, proposing a uniform approach for generating solutions while also providing human-understandable explanations. Current state of the art solvers of semantic problems rely on traditional machine learning methods. Therefore their results are not easily reusable by algorithms or interpretable by humans. We propose leveraging web-scale knowledge graphs to determine a semantic frame of interpretation. Semantic knowledge graphs are graphs in which nodes represent concepts and the edges represent the relations between them. Our approach has the following advantages: (1) it reduces the space in which the problem is to be solved; (2) sparse and noisy data can be used without relying only on the relations deducible from the data itself; (3) the output of the inference algorithm is supported by an interpretable justification. We demonstrate our approach in two domains: (1) Topic Modeling: We form topics using connectivity in semantic graphs. We use the same topic models for two very different recommendation systems, one designed for high noise interactive applications and the other for large amounts of web data. (2) Analogy Solving: For humans, analogies are a fundamental reasoning pattern, which relies on abstraction and comparative analysis. In order for an analogy to be understood, precise relations have to be identified and mapped. We introduce graph algorithms to assess the analogy strength in contexts derived from the analogy words. We demonstrate our approach by solving standardized test analogy question.
【Keywords】: Semantic; Context; Analogy; Topic
【Paper Link】 【Pages】:3065-3066
【Authors】: Tim Brys ; Ann Nowé
【Abstract】: This extended abstract provides a brief overview of my PhD research on multi-objectivization and ensemble techniques in reinforcement learning.
【Keywords】: Reinforcement Learning; Multi-Objectivization; Ensemble Techniques
【Paper Link】 【Pages】:3067-3068
【Authors】: Maria de los Angeles Chang
【Abstract】: A major challenge in artificial intelligence is building intelligent, interactive learning environments that can support students in human-like ways. Analogical reasoning can be a catalyst for conceptual learning, yet very few systems support analogical reasoning as an instructional activity. In my thesis, I plan to demonstrate that an analogy tutor can assist conceptual learning by guiding students through instructional comparisons.
【Keywords】: intelligent tutoring systems, analogical reasoning, spatial reasoning, qualitative reasoning
【Paper Link】 【Pages】:3069-3070
【Authors】: John A. Doucette
【Abstract】: A novel technique for deciding the outcome of an election when only partial preference ballots are submitted, using machine learning. An explicit connection between machine learning and social choice is discovered, which suggests many possible avenues of future work.
【Keywords】: Partial Preferences; Imputation; Machine Learning
【Paper Link】 【Pages】:3071-3072
【Authors】: Elizabeth A. Jensen
【Abstract】: In the event of an earthquake or fire, search and rescue efforts may be delayed until it is safe for a human team to enter the area. A team of robots could enter in advance to provide maps, images and locations of interest to the human team, allowing them to prepare their approach when they can enter. In a disaster area, communication may also be limited. We have developed a set of distributed algorithms that make use of a small number of robots to fully explore an unknown environment even with restrictions on communication, team size, and available sensors. We show, through proofs and experiments, that the algorithm will allow the team of robots to fully explore the environment and maintain the necessary communication to return the information to the search and rescue team waiting outside.
【Keywords】: Multi-Agent Systems; Multi-Robot Systems; Exploration
【Paper Link】 【Pages】:3073-3074
【Authors】: Anup K. Kalia
【Abstract】: We provide an approach to estimate trust between agents from their interactions. Our approach takes a probabilistic model of trust founded on commitments. We assume commitments to estimate trust because a commitment describes what an agent may expect of another. Therefore, the satisfaction or violation of a commitment provides a natural basis for determining how much to trust another agent. We evaluate our approach empirically. In one study, 30 subjects read emails extracted from the Enron dataset augmented with some synthetic emails to capture commitment operations missing in the Enron corpus. The subjects estimated trust between each pair of communicating participants. We trained model parameters for each subject with respect to our automated analysis of the emails, showing that our trained parameters yield a lower prediction error of a subject's trust rating given automatically inferred commitments than fixed parameters.
【Keywords】: Algorithms, Commitments, Trust,
【Paper Link】 【Pages】:3075-3076
【Authors】: Taraneh Khazaei
【Abstract】: This manuscript provides the research questions, proposed research plans, as well as expected contributions of my doctoral dissertation. My dissertation is primarily focused on providing computational approaches to study and analyze dialectical reasoning in large-scale online platforms. In particular, I aim to tackle the challenge of developing novel models to automatically classify explanation and argumentation as two different types of reasoning in text of discourse on the Web. The resulting models can be incorporated in the social Web environments to increase participants' awareness of others' reasoning types, which may lead to a more effective dialogue protocol and strategy.
【Keywords】: text mining; collective intelligence; social computing; online deliberation
【Paper Link】 【Pages】:3077-3078
【Authors】: Khang Nhut Lam
【Abstract】: The thesis proposes creating bilingual dictionaries andWordnets for languages without many lexical resourcesusing resources of resource-rich languages. Our workwill have the advantage of creating lexical resources,reducing time and cost and at the same time improvingthe quality of resources created.
【Keywords】: lexical resources; bilingual dictionaries; Wordnets
【Paper Link】 【Pages】:3079-3080
【Authors】: Luis Enrique Pineda
【Abstract】: Markov decision processes (MDP) offer a rich model that has been extensively used by the AI community for planning and learning under uncertainty. However, solving MDPs is often intractable, which has led to the development of many approximate algorithms. In my dissertation work I introduce a new paradigm to handle this complexity by defining a family of MDP reduced models characterized by two parameters: the maximum number of primary outcomes per action that are fully accounted for and the maximum number of occurrences of the remaining exceptional outcomes that are planned for in advance. Reduced models can be solved much faster using heuristic search algorithms, benefiting from the dramatic reduction in the number of reachable states. This framework places recent work on MDP determinization in a broader context and lays the foundation for efficient and systematic exploration of the space of MDP model reductions. Progress so far work includes a formal definition of this family of MDP reductions, a continual planning paradigm to handle the case when the number of exceptions reaches the maximum allowed, a simple greedy approach to generate good reductions for a given planning domain, and a compilation scheme that generates MDP reductions from a PPDDL description of a planning problem.
【Keywords】: Markov Decision Processes; Determinization; Continual Planning; Planning Under Uncertainty
【Paper Link】 【Pages】:3081-3082
【Authors】: Matthew Spradling
【Abstract】: We have introduced a new model of hedonic coalition formation game, which we call Roles and Teams Hedonic Games (RTHG). In this model, agents view coalitions as compositions of available roles. An agent's utility for a partition is based upon which role she fulfills within the coalition and which roles are being fulfilled within the coalition. The major contributions of the paper include designing the RTHG model, with its corresponding stability and (NP-hard) optimization criteria, designing a heuristic partitioning algorithm and local search algorithm, implementation and testing.
【Keywords】: coalition formation, computational complexity, hedonic games, optimization
【Paper Link】 【Pages】:3083-3084
【Authors】: Ran Taig
【Abstract】: The main focus of our work is the use of classical planning algorithms in service of more complex problems of planning under uncertainty. In particular, we are exploring compilation techniques that allow us to reduce some probabilistic planning problems into variants of classical planning, such as metric planning,resource-bounded planning, and cost-bounded suboptimal planning. Currently, our initial work focuses on \emph{conformant probabilistic planning}. We intend toimprove our current methods by improving our compilation methods, but also by improving the ability of current planners to handle the special features ofour compiled problems. Then, we hope to extend these techniques to handle more complex probabilistic settings, such as problems with stochastic actions andpartial observability.
【Keywords】: planning under uncertainty, translation based approach,conformant probabilistic planning
【Paper Link】 【Pages】:3085-3086
【Authors】: Lawson L. S. Wong
【Abstract】: Mobile-manipulation robots performing service tasks in human-centric indoor environments has long been a dream for developers of autonomous agents. Tasks such as cooking and cleaning require interaction with the environment, hence robots need to know relevant aspects of their spatial surroundings. However, unlike the structured settings that industrial robots operate in, service robots typically have little prior information about their environment. Even if this information was given, due to the involvement of many other agents (e.g., humans moving objects), uncertainty in the complete state of the world is inevitable over time. Additionally, most information about the world is irrelevant to any particular task at hand. Mobile manipulation robots therefore need to continuously perform the task of state estimation, using perceptual information to maintain the state, and its uncertainty, of task-relevant aspects of the world. Because indoor tasks frequently require the use of objects, objects should be given critical emphasis in spatial representations for service robots. Compared to occupancy grids and feature-based maps often used in navigation and SLAM, object-based representations are arguably still in their infancy. In my thesis, I propose a representation framework based on objects, their 'semantic' attributes, and their geometric realizations in the physical world.
【Keywords】: State estimation, world modeling
【Paper Link】 【Pages】:3087-3089
【Authors】: Xiaojian Wu
【Abstract】: Many dynamic phenomena can be modeled as a diffusion process. For my dissertation, I study diffusion processes in the area of sustainability, such as how wildlife spreads over a fragmental landscape and how fish spread within a river network, and try to answer two important questions. 1) How to shape the diffusion by using a limited amount of resources, for example how to maximize the spread of birds by preserving a limited number of landscape units? 2) How to model the diffusion process and estimate the parameters of the model using incomplete and noisy observations? This document describes my current research progress and future research directions of answering these two important questions.
【Keywords】:
【Paper Link】 【Pages】:3090-3091
【Authors】: Saad Alqithami ; Henry Hexmoor
【Abstract】: In a dynamic network organization, member agents usually interact to coordinate their actions and to cooperate towards a common goal with which they have no previous experience. These relations allow them to produce a cohesive group to build and maintain their network. This paper will outline the effect of social capital on a network structure inside a network organization.
【Keywords】: social capital; multi-agent systems; network organizations.
【Paper Link】 【Pages】:3092-3093
【Authors】: Ofra Amir ; Barbara J. Grosz ; Roni Stern
【Abstract】: This paper defines the "Single Agent in a Team Decision" (SATD) problem. SATD differs from prior multi-agent communication problems in the assumptions it makes about teammates' knowledge of each other's plans and possible observations. The paper proposes a novel integrated logical-decision-theoretic approach to solving SATD problems, called MDP-PRT. Evaluation of MDP-PRT shows that it outperforms a previously proposed communication mechanism that did not consider the timing of communication and compares favorably with a coordinated Dec-POMDP solution that uses knowledge about all possible observations.
【Keywords】: multi-agent systems;information sharing
【Paper Link】 【Pages】:3094-3095
【Authors】: Nobuo Araki ; Masakazu Muramatsu ; Kunihito Hoki ; Satoshi Takahashi
【Abstract】: In this paper, we propose a new learning method sim- ulation adjusting that adjusts simulation policy to im- prove the move decisions of the Monte Carlo method. We demonstrated simulation adjusting for 4 × 4 board Go problems. We observed that the rate of correct an- swers moderately increased.
【Keywords】: Monte-Carlo;Go;Balancing;Adjusting
【Paper Link】 【Pages】:3096-3097
【Authors】: Amos Azaria ; Ya'akov Gal ; Claudia V. Goldman ; Sarit Kraus
【Abstract】: Choice selection processes are a family of bilateral games of incomplete information in which a computer agent generates advice for a human user while considering the effect of the advice on the user's behavior in future interactions. The human and the agent may share certain goals, but are essentially self-interested. This paper extends selection processes to settings in which the actions available to the human are ordered and thus the user may be influenced by the advice even though he doesn't necessarily follow it exactly. In this work we also consider the case in which the user obtains some observation on the sate of the world. We propose several approaches to model human decision making in such settings. We incorporate these models into two optimization techniques for the agent advice provision strategy. In the first one the agent used a social utility approach which considered the benefits and costs for both agent and person when making suggestions. In the second approach we simplified the human model in order to allow modeling and solving the agent strategy as an MDP. In an empirical evaluation involving human users on AMT, we showed that the social utility approach significantly outperformed the MDP approach.
【Keywords】: Human modeling; advice provision; persuasion
【Paper Link】 【Pages】:3098-3099
【Authors】: Rogelio Enrique Cardona-Rivera ; Robert Michael Young
【Abstract】: We present work toward computationally defining a model of narrative comprehension vis-à-vis memory of narrative events, via an automated planning knowledge representation, capable of being used in a narrative generation context.
【Keywords】: computational models of narrative; knowledge representation; classical planning; narrative comprehension; cognitive psychology; memory
【Paper Link】 【Pages】:3100-3101
【Authors】: Peng Cheng ; Jeng-Shyang Pan
【Abstract】: Today, people benefit from utilizing data mining technologies, such as association rule mining methods, to find valuable knowledge residing in a large amount of data. However, they also face the risk of exposing sensitive or confidential information, when data is shared among different organizations. Thus, a question arise: how can we prevent that sensitive knowledge is discovered, while ensuring that ordinary non-sensitive knowledge can be mined to the maximum extent possible. In this paper, we address the problem of privacy preserving in association rule mining from the perspective of multi-objective optimization. A new hiding method based evolutionary multi-objective optimization (EMO) is proposed and the side effects generated by the hiding process are formulated as optimization goals. EMO is used to find candidate transactions to modify so that side effects are minimized. Comparative experiments with exact methods on real datasets demonstrated that the proposed method can hide sensitive rules with fewer side effects.
【Keywords】: Association rule hiding, evolutionary multi-objective optimization, EMO
【Paper Link】 【Pages】:3102-3103
【Authors】: Yili Fang ; Hailong Sun ; Richong Zhang ; Jinpeng Huai ; Yongyi Mao
【Abstract】: One of the most important crowdsourcing topics is to study the effective quality control methods so as to reduce the cost and to guarantee the quality of task processing. As an effective approach, iterative improvement workflow is known to choose the best result from multiple workflows. However, for complex crowdsourcing tasks that consists of a certain number of subtasks under some specific constraints, but cannot be split into subtasks to be crowdsourced, the approach merely considers the best workflow without integrating the contributions of all workflows, which potentially results in extra costs for more iterations. In this paper, we propose an assembly model to integrate the best output of subtasks from different workflows. Moreover, we devise an efficient iterative method based on POMDP to improve the quality of assembled output. Empirical studies confirms the superiority of our proposed model.
【Keywords】: Crowdsourcing; iterative improvement workflow;POMDP
【Paper Link】 【Pages】:3104-3105
【Authors】: Tadhg Fitzgerald ; Barry O'Sullivan ; Yuri Malitsky ; Kevin Tierney
【Abstract】: This paper outlines an online approach for algorithm configuration which uses the power of modern multicore system to evaluate multiple parameters configurations in parallel.
【Keywords】: Real-time configuration; algorithm configuration; online configuration
【Paper Link】 【Pages】:3106-3107
【Authors】: Rafik Hadfi ; Takayuki Ito
【Abstract】: There has been a great deal of interest about negotiations having interdependent issues and nonlinear utility spaces as they arise in many realistic situations. In this case, reaching a consensus among agents becomes more difficult as the search space and the complexity of the problem grow. Nevertheless, none of the proposed approaches tries to quantitatively assess the complexity of the scenarios in hand, or to exploit the topology of the utility space necessary to concretely tackle the complexity and the scaling issues. We address these points by adopting a representation that allows a modular decomposition of the issues and constraints by mapping the utility space into an issue-constraint hypergraph. Exploring the utility space reduces then to a message passing mechanism along the hyperedges by means of utility propagation. Adopting such representation paradigm will allow us to rigorously show how complexity arises in nonlinear scenarios. To this end, we use the concept of information entropy in order to measure the complexity of the hypergraph. Being able to assess complexity allows us to improve the message passing algorithm by adopting a low-complexity propagation scheme. We evaluated our model using parametrized random hyper- graphs, showing that it can optimally handle complex utility spaces while outperforming previous sampling approaches.
【Keywords】: Multi-issue Negotiation; Interdependence; Nonlinear Utility; Constraint-based utility spaces; Complexity; Hyper-Graph; Max-Sum; Utility Propagation
【Paper Link】 【Pages】:3108-3109
【Authors】: Elizabeth A. Jensen ; Ken Sugawara
【Abstract】: We propose a novel algorithm that allows a small team of robots to fully explore an unknown environment, even in the face of extreme communication restrictions. We verify, through proofs and experiments, that the algorithm will achieve full exploration.
【Keywords】: Multi-Agent Systems; Multi-Robot Systems; Exploration
【Paper Link】 【Pages】:3110-3111
【Authors】: Stephen Kelly ; Malcolm I. Heywood
【Abstract】: Keepaway soccer is a challenging robot control task that has been widely used as a benchmark for evaluating multi-agent learning systems. The majority of research in this domain has been from the perspective of reinforcement learning (function approximation) and neuroevolution. One of the challenges under multi-agent tasks such as keepaway is to formulate effective mechanisms for diversity maintenance. Indeed the best results to date on this task utilize some form of neuroevolution with genotypic diversity. In this work, a symbiotic framework for evolving teams of programs is utilized with both genotypic and behavioural forms of diversity maintenance considered. Specific contributions of this work include a simple scheme for characterizing genotypic diversity under teams of programs and its comparison to behavioural formulations for diversity under the keepaway soccer task. Unlike previous research concerning diversity maintenance in genetic programming (GP), we are explicitly interested in solutions taking the form of teams of programs.
【Keywords】: genetic programming; symbiosis; multi-agent learning; diversity
【Paper Link】 【Pages】:3112-3113
【Authors】: Igor Kiselev ; Pascal Poupart
【Abstract】: As a promising alternative to using standard (often intractable) planning techniques with Bellman equations, we propose an interesting method of optimizing POMDP controllers by probabilistic inference in a novel equivalent single-DBN generative model. Our inference approach to POMDP planning allows for (1) for application of various techniques for probabilistic inference in single graphical models, and (2) for exploiting the factored structure in a controller architecture to take advantage of natural structural constrains of planning problems and represent them compactly. Our contributions can be summarized as follows: (1) we designed a novel single-DBN generative model that ensures that the task of probabilistic inference is equivalent to the original problem of optimizing POMDP controllers, and (2) we developed several inference approaches to approximate the value of the policy when exact inference methods are not tractable to solve large-size problems with complex graphical models. The proposed approaches to policy optimization by probabilistic inference are evaluated on several POMDP benchmark problems and the performance of the implemented approximation algorithms is compared.
【Keywords】: POMDP planning; Finite State Controllers; probabilistic inference; control policy; Loopy Belief Propagation algorithm; Factored Frontier algorithm
【Paper Link】 【Pages】:3114-3115
【Authors】: Sylvain Labranche ; Eric Beaudry
【Abstract】: In real world planning problems, it might not be possible for an automated agent to satisfy all the objectives assigned to it because available resources are limited. When objectives cannot all be satisfied, classical planning returns no plan. In partial satisfaction planning, it is possible to satisfy only a subset of the objectives. To solve this kind of problems, an agent could select the objectives subset and the plan that maximizes the net benefit, i.e. the sum of satisfied objectives utilities minus the sum of the cost of actions. This approach has been experimented for deterministic planning. This paper extends partial satisfaction planning for problems with uncertainty on time. For problems under uncertainty, the best subset of objectives may not be calculated at planning time. The effective duration of actions at execution time may dynamically influence the achievable subset of objectives. Our approach introduces special actions to explicitly abort objectives. This enables control on when an objective is aborted.
【Keywords】: Planning
【Paper Link】 【Pages】:3116-3117
【Authors】: Chia-Ling Lee ; Ya-Ning Chang ; Chao-Lin Liu ; Chia-Ying Lee ; Jane Yung-jen Hsu
【Abstract】: A Chinese character embedded in different compound words may carry different meanings. In this paper, we aim at semantical clustering of a given family of morphologically related Chinese words. In Experiment 1, we employed linguistic features at the word, syntactic, semantic, and contextual levels in aggregated computational linguistics methods to handle the clustering task. In Experiment 2, we recruited adults and children to perform the clustering task. Experimental results indicate that our computational model achieved a similar level of performance as children.
【Keywords】: Chinese compounds; semantic clustering; morphology
【Paper Link】 【Pages】:3118-3119
【Authors】: Chi-Chin Lin ; Jane Yung-jen Hsu
【Abstract】: Humorous images can be seen in many social media websites. However, newcomers to these websites often have trouble fitting in because of the subculture among the community is usually implicit. Among all the types of humorous images, Internet memes are relatively hard for newcomers to understand. In this work, we develop a system leveraging crowdsourcing technique to generate explanations for meme images. We claim that people who are not familiar with Internet meme subculture can still quickly pick up the gist of the memes through reading the explanations. Our template-based explanation can illustrate the incongruity between normal situations and the punchlines in jokes. The explanations can be produced by going through 2 designed humor tasks. In our pilot study, acceptable explanations for 5 unique memes are generated. For further study, generating explanations for more general text jokes are possible.
【Keywords】: computational humor recognition; crowdsourcing; Internet memes; template-based explanations
【Paper Link】 【Pages】:3120-3121
【Authors】: Dawei Liu ; Yuanzhuo Wang ; Yantao Jia ; Jingyuan Li ; Zhihua Yu
【Abstract】: One challenge of link prediction in online social networks is the large scale of many such networks. The measures used by existing work lack a computational consideration in the large scale setting. We propose the notion of social distance in a multi-dimensional form to measure the closeness among a group of people in Microblogs. We proposed a fast hashing approach called Locality-sensitive Social Distance Hashing (LSDH), which works in an unsupervised setup and performs approximate near neighbor search without high-dimensional distance computation. Experiments were applied over a Twitter dataset and the preliminary results testified the effectiveness of LSDH in predicting the likelihood of future associations between people.
【Keywords】: link prediction; locality-sensitive hashing; social distance
【Paper Link】 【Pages】:3122-3123
【Authors】: Nian Liu ; Lin Li ; Guandong Xu ; Zhenglu Yang
【Abstract】: Users of a social network like to follow the posts published by influential users. Such posts usually are delivered quickly and thus will produce a strong influence on public opinions. In this paper, we focus on the problem of identifying domain-dependent influential users(or topic experts). Some of traditional approaches are based on the post contents of users user’s to identify influential users, which may be biased by spammers who try to make posts related to some topics through a simple copy and paste. Others make use of user authentication information given by a service platform or user self description (introduction or label) in finding influential users. However, what users have published is not necessarily related to what they have registed and described. In addition, if there is no comments from other users, it’s less objective to assess a user’s post quality. To improve effectiveness of recognizing influential users in a topic of microblogs, we propose a post-feature based approach which is supplementary to post-content based approaches. Our experimental results show that the post-feature based approach produces relatively higher precision than that of the content based approach.
【Keywords】: influential user; microblog; post feature
【Paper Link】 【Pages】:3124-3125
【Authors】: Yuan Liu ; Siyuan Liu ; Jie Zhang ; Hui Fang ; Han Yu ; Chunyan Miao
【Abstract】: Reputation models depend on the ratings provided by buyers togauge the reliability of sellers in multi-agent based e-commerce environment. However, there is no prevention forthe cases in which a buyer misjudges a seller, and provides a negative rating to an original satisfactory transaction. In this case,how should the seller get his reputation repaired andutility loss recovered? In this work, we propose a mechanism to mitigate the negativeeffect of the misreported ratings. It temporarily inflates the reputation of thevictim seller with a certain value for a period of time. This allows the seller to recover hisutility loss due to lost opportunities caused by the misreported ratings. Experiments demonstrate the necessity and effectiveness of the proposed mechanism.
【Keywords】:
【Paper Link】 【Pages】:3126-3127
【Authors】: Yuan Liu ; Jie Zhang ; Han Yu ; Chunyan Miao
【Abstract】: Truthful bidding is a desirable property for continuous double auctions (CDAs). Many incentive mechanisms have been proposed to elicit truthful bids. However, existing truthful CDA mechanisms often overlook the possibility that sellers may choose not to deliver the auctioned items to buyers as promised. In this situation, buyers may become unwilling to bid their true valuations in the future to compensate for their risks of being cheated, thereby rendering CDAs ineffective. In this paper, we propose a novel reputation-aware CDA (named RCDA) mechanism to consider the honesty of auction participants. It dynamically adjusts bids and asks according to the reputation of participants to reflect the risks involved in the transactions. Theoretical analysis proves that RCDA is effective in eliciting truthful bids from buyers and sellers in the presence of possible dishonest behavior from both buyers and sellers.
【Keywords】:
【Paper Link】 【Pages】:3128-3129
【Authors】: Jian Luo ; Fuan Pu ; Yulai Zhang ; Guiming Luo
【Abstract】: The knowledgebase uncertainty and the argument preferences are considered in this paper. The uncertainty is captured by weighted satisfiability degree, while a preference relation over arguments is derived by the beliefs of an agent.
【Keywords】: Argument;Beliefs; Preference; Satisfiability Degree
【Paper Link】 【Pages】:3130-3131
【Authors】: Zhi Qiao ; Peng Zhang ; Chuan Zhou ; Yanan Cao ; Li Guo ; Yanchuan Zhang
【Abstract】: With the rapid growth of event-based social networks, the demand of event recommendation becomes increasingly important. Different from classic recommendation problems, event recommendation generally faces the problems of heterogenous online and offline social relationships among users and implicit feedback data. In this paper, we present a baysian probability model that can fully unleash the power of heterogenous social relations and efficiently tackle with implicit feedback characteristic for event recommendation. Experimental results on several real-world datasets demonstrate the utility of our method.
【Keywords】: event recommendation
【Paper Link】 【Pages】:3132-3133
【Authors】: Zeynep Gozen Saribatur ; Esra Erdem ; Volkan Patoglu
【Abstract】: We consider multiple teams of heterogeneous robots, where each team is given a feasible task to complete in its workspace on its own, and where teams are allowed to transfer robots between each other. We study the problem of finding a coordination of robot transfers between teams to ensure an optimal global plan (with minimum makespan) so that all tasks can be completed as soon as possible by helping each other. We propose to solve this problem using answer set programming.
【Keywords】: multi-robot teams; optimal coordination; answer set programming
【Paper Link】 【Pages】:3134-3135
【Authors】: Daniel R. Schlegel ; Stuart C. Shapiro
【Abstract】: Hybrid reasoners combine multiple types of reasoning, usually subsumption and Prolog-style resolution. We outline a system which combines natural deduction and subsumption reasoning using Inference Graphs implementing a Logic of Arbitrary and Indefinite Objects.
【Keywords】: Inference Graphs; Automated Reasoning; Logic; L_A; Logic of Arbitrary and Indefinite Objects
【Paper Link】 【Pages】:3136-3137
【Authors】: Vishnu Purushothaman Sreenivasan ; Haitham Bou-Ammar ; Eric Eaton
【Abstract】: We develop an online multi-task formulation of model-based gradient temporal-difference (GTD) reinforcement learning. Our approach enables an autonomous RL agent to accumulate knowledge over its lifetime and efficiently share this knowledge between tasks to accelerate learning. Rather than learning a policy for a reinforcement learning task tabula rasa, as in standard GTD, our approach rapidly learns a high performance policy by building upon the agent's previously learned knowledge. Our preliminary results on controlling different mountain car tasks demonstrates that GTD-ELLA significantly improves learning over standard GTD(0).
【Keywords】: online multi-task learning; lifelong learning; reinforcement learning; gradient temporal-difference learning
【Paper Link】 【Pages】:3138-3139
【Authors】: Roberto Valerio ; Ricardo Vilalta
【Abstract】: We describe a data complexity approach to kernel selection based on the behavior of polynomial and Gaussian kernels. Our resultsshow how the use of a Gaussian kernel produces a gram matrix with useful local information that has no equivalent counterpart inpolynomial kernels.By exploiting neighborhood information embedded by data complexity measures, we are able to carry out a form of meta-generalization.Our goal is to predict which data sets are more favorable to particular kernels (Gaussian or polynomial).The end result is a framework to improve the model selection process in Support Vector Machines.
【Keywords】: data complexity measures, model selection, kernel methods, support vector machines, polynomial kernel, Gaussian kernel
【Paper Link】 【Pages】:3140-3141
【Authors】: Lawson L. S. Wong
【Abstract】: Robots performing service tasks such as cooking and cleaning in human-centric environments require knowledge of certain environmental states in order to complete tasks successfully. While much effort has gone into developing various estimators for deriving distributions on values of unknown states, less attention has been placed on why the particular estimation problem arises. In this work, I argue that state estimation should no longer be treated as a black box. Estimating large sets of variables is computationally costly; just because a technique exists to estimate the values of certain variables does not justify its application. For robots whose ultimate mission is to complete tasks, only variables that are relevant to successful completion should be estimated. I propose to initially only track a minimal set of directly-relevant variables (attention), and gradually increase the sophistication of models on-demand (refinement), in a local fashion. This estimator refinement process is triggered by violations in expectations of task success (mismatch). This model selection framework is demonstrated through a proof-of-concept case study.
【Keywords】: State estimation, model selection
【Paper Link】 【Pages】:3142-3143
【Authors】: Jia Xu ; Ubbo Visser ; Mansur R. Kabuka
【Abstract】: Instance checking is considered a central service for data retrieval from description logic (DL) ontologies. In this paper, we propose a revised most specific concept (MSC) method for DL SHI}, which converts instance checking into subsumption problems. This revised method can generate small concepts that are specific-enough to answer a given query, and allow reasoning to explore only a subset of the ABox data to achieve efficiency. Experiments show effectiveness of our proposed method in terms of concept size reduction and the improvement in reasoning efficiency.
【Keywords】: Description Logic; Ontology; Object Query; MSC
【Paper Link】 【Pages】:3144-3145
【Authors】: Yexiang Xue ; Stefano Ermon ; Carla P. Gomes ; Bart Selman
【Abstract】: A key strategy for speeding up computation is to run in parallel on multiple cores. However, on hard combinatorial problems, exploiting parallelism has been surprisingly challenging. It appears that traditional divide-and-conquer strategies do not work well, due to the intricate non-local nature of the interactions between the problem variables. In this paper, we introduce a novel way in which parallelism can be used to exploit hidden structure of hard combinatorial problems. We demonstrate the success of this approach on minimal set basis problem, which has a wide range of applications in machine learning and system security, etc. We also show the effectiveness on a related application problem from materials discovery. In our approach, a large number of smaller sub-problems are identified and solved concurrently. We then aggregate the information from those solutions, and use this to initialize the search of a global, complete solver. We show that this strategy leads to a significant speed-up over a sequential approach. The strategy also greatly outperforms state-of-the-art incomplete solvers in terms of solution quality. Our work opens up a novel angle for using parallelism to solve hard combinatorial problems.
【Keywords】: Combinatorial Search; Parallel Algorithm; NP-complete Problem; Set Basis Problem
【Paper Link】 【Pages】:3146-3147
【Authors】: Jinfeng Yang ; Yi Guan ; Xishuang Dong ; Bin He
【Abstract】: Similarity between words is becoming a generic problem for many applications of computational linguistics, and computing word similarities is determined by word representations. Inspired by the analogies between words and lymphocytes, a lymphocyte-style word representation is proposed. The word representation is built on the basis of dependency syntax of sentences and represent word context as head properties and dependent properties of the word. Lymphocyte-style word representations are evaluated by computing the similarities between words, and experiments are conducted on the Penn Chinese Treebank 5.1. Experimental results indicate that the proposed word representations are effective.
【Keywords】: lymphocyte-style word representations; word agent; similarity
【Paper Link】 【Pages】:3148-3149
【Authors】: Yingzhen Yang ; Zhangyang Wang ; Jianchao Yang ; Jiangping Wang ; Shiyu Chang ; Thomas S. Huang
【Abstract】: L1-Graph has been proven to be effective in data clustering, which partitions the data space by using the sparse representation of the data as the similarity measure. However, the sparse representation is performed for each datum separately without taking into account the geometric structure of the data. Motivated by L1-Graph and manifold leaning, we propose Laplacian Regularized L1-Graph (LRℓ1-Graph) for data clustering. The sparse representations of LRℓ1-Graph are regularized by the geometric information of the data so that they vary smoothly along the geodesics of the data manifold by the graph Laplacian according to the manifold assumption. Moreover, we propose an iterative regularization scheme, where the sparse representation obtained from the previous iteration is used to build the graph Laplacian for the current iteration of regularization. The experimental results on real data sets demonstrate the superiority of our algorithm compared to L1-Graph and other competing clustering methods.
【Keywords】: Clustering; Manifold Assumption; Laplacian Regularized L1-Graph
【Paper Link】 【Pages】:3150-3151
【Authors】: Yulai Zhang ; Guiming Luo
【Abstract】: Algorithm's time complexity is an essential issue for time series prediction in numerous practices.A novel fast exact inference method for Gaussian process model is proposed in this paper to accelerate the task of non-stationary time series prediction. Experiment was done on the real world power load data.
【Keywords】:
【Paper Link】 【Pages】:3152-3153
【Authors】: Yulai Zhang ; Guiming Luo
【Abstract】: Inferring the causal direction between two variables is a nontrivial problem in the subject of causal discovery from observed data. A method for errors-in-variables models where both the cause variable and the effect variable are observed with measurement errors is presented in this paper.
【Keywords】:
【Paper Link】 【Pages】:3154-
【Authors】: Zeya Zhao ; Yantao Jia ; Yuanzhuo Wang
【Abstract】: Relation inference between concepts in knowledge base has been extensively studied in recent years. Previous methods mostly apply the relations in the knowledge base, without fully utilizing the contents, i.e., the attributes of concepts in knowledge base. In this paper, we propose a content-structural relation inference method (CSRI) which integrates the content and structural information between concepts for relation inference. Experiments on data sets show that CSRI obtains 15% improvement compared with the state-of-the-art methods.
【Keywords】: relation inference, Knowledge Bases, attributes, relation path