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Tools with Artificial Intelligence (ICTAI), 2013 IEEE 25th International Conference on

Date 4-6 Nov. 2013

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Displaying Results 1 - 25 of 163
  • [Front cover]

    Publication Year: 2013 , Page(s): C4
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  • [Title page i]

    Publication Year: 2013 , Page(s): i
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  • [Title page iii]

    Publication Year: 2013 , Page(s): iii
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  • [Copyright notice]

    Publication Year: 2013 , Page(s): iv
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  • Table of contents

    Publication Year: 2013 , Page(s): v - xvi
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  • ICTAI 2013 General Chair's Message

    Publication Year: 2013 , Page(s): xvii
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  • ICTAI 2013 Program Chair's Message

    Publication Year: 2013 , Page(s): xviii
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  • ICTAI 2013 Conference Committees

    Publication Year: 2013 , Page(s): xix - xx
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  • ICTAI 2013 Reviewers

    Publication Year: 2013 , Page(s): xxi - xxiv
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  • Special Track on SAT and CSP Technologies Foreword

    Publication Year: 2013 , Page(s): xxv
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  • Special Track on SAT and CSP Technologies Organizers

    Publication Year: 2013 , Page(s): xxvi
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  • Special Track on SAT and CSP Technologies Additional Reviewers

    Publication Year: 2013 , Page(s): xxvii
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  • An Automatic Algorithm Selection Approach for Planning

    Publication Year: 2013 , Page(s): 1 - 8
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (215 KB) |  | HTML iconHTML  

    Despite the advances made in the last decade in automated planning, no planner outperforms all the others in every known benchmark domain. This observation motivates the idea of selecting different planning algorithms for different domains. Moreover, the planners' performances are affected by the structure of the search space, which depends on the encoding of the considered domain. In many domains... View full abstract»

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  • Modulo Based CNF Encoding of Cardinality Constraints and Its Application to MaxSAT Solvers

    Publication Year: 2013 , Page(s): 9 - 17
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (667 KB) |  | HTML iconHTML  

    Totalizer (TO) by Bailleux et al. and Half Sorting Network (HS) by Asin et al. are typical CNF encoding methods of cardinality constraint. The former is based on unary adder, while the latter is based on odd-even merge. Although TO is inferior to HS in terms of the number of clauses, TO is superior to HS in terms of the number of variables. We propose a new method called Modulo Totalizer (MTO) to ... View full abstract»

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  • Learnability of Specific Structural Patterns of Planning Problems

    Publication Year: 2013 , Page(s): 18 - 23
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (203 KB) |  | HTML iconHTML  

    In Automated planning, learning and exploiting additional knowledge within a domain model, in order to improve the performance of domain-independent planners, has attracted much research. Reformulation techniques such as those based on macro-operators or entanglements are very promising because they are, to some extent, domain model and planning engine independent. Despite the significant amount o... View full abstract»

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  • HNNP - A Hybrid Neural Network Plait for Improving Image Classification with Additional Side Information

    Publication Year: 2013 , Page(s): 24 - 29
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (245 KB) |  | HTML iconHTML  

    Most of the artificial intelligence and machine learning researches deal with big data today. However, there are still a lot of real world problems for which only small and noisy data sets exist. Hence, in this paper we focus on those small data sets of noisy images. Applying learning models to such data may not lead to the best possible results because of few and noisy training examples. We propo... View full abstract»

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  • On Finding Approximate Solutions of Qualitative Constraint Networks

    Publication Year: 2013 , Page(s): 30 - 37
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (1444 KB) |  | HTML iconHTML  

    Qualitative Spatial and Temporal Reasoning (QSTR) represents spatial and temporal information in terms of human comprehensible qualitative predicates and reasons about qualitative information by solving qualitative constraint networks (QCNs). Despite significant progress in the past three decades, more and more evidence has shown that it is inherently hard to find exact solutions for expressive qu... View full abstract»

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  • Three-Valued Possibilistic Networks: Semantics & Inference

    Publication Year: 2013 , Page(s): 38 - 45
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (241 KB) |  | HTML iconHTML  

    Possibilistic networks are belief graphical models based on possibility theory. This paper deals with a special kind of possibilistic networks called three-valued possibilistic networks where only three possibility levels are used to encode uncertain information. The paper analyzes different semantics of three-valued networks and provides precise relationships relating the different semantics. Mor... View full abstract»

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  • On the Propagation Strength of SAT Encodings for Qualitative Temporal Reasoning

    Publication Year: 2013 , Page(s): 46 - 54
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (304 KB) |  | HTML iconHTML  

    Several studies in Qualitative Spatial and Temporal Reasoningdiscuss translations of the satisfiability problem on qualitativeconstraint languages into propositional SAT. Most of these encodings focus on compactness, while propagation strength is seldom discussed. In this work, we focus on temporal reasoning with the Point Algebra and Allen's Interval Algebra. We understand all encodings as a comb... View full abstract»

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  • Part-Whole Relations as Products of Metric Spaces

    Publication Year: 2013 , Page(s): 55 - 62
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (224 KB) |  | HTML iconHTML  

    Humans recognize objects by combining the processes of composition/decomposition and similarity matching. When we see an object, we try to match its whole, global aspect to some internal similar pattern that describes it. If this is not enough to recognize the object, it is decomposed into a structure of parts, where each part is considered as a whole to be matched. In this paper, we propose a for... View full abstract»

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  • ICAMF: Improved Context-Aware Matrix Factorization for Collaborative Filtering

    Publication Year: 2013 , Page(s): 63 - 70
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (199 KB) |  | HTML iconHTML  

    Context-aware recommender system (CARS) can provide more accurate rating predictions and more relevant recommendations by taking into account the contextual in-formation. Yet the state-of-the-art context-aware matrix factorization approaches only consider the influence of con-textual information on item bias. Tensor factorization based Multiverse Recommendation deals with the contextual in-formati... View full abstract»

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  • Optimized Deep Learning Architectures with Fast Matrix Operation Kernels on Parallel Platform

    Publication Year: 2013 , Page(s): 71 - 78
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (411 KB) |  | HTML iconHTML  

    In this paper, we introduce an optimized deep learning architecture with flexible layer structures and fast matrix operation kernels on parallel computing platform (e.g. NVIDIA's GPU). Carefully designed layer-wise strategies are conducted to integrate different kinds of deep architectures into a uniform neural training-testing system. Our fast matrix operation kernels are implemented in deep arch... View full abstract»

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  • Error Allowing Minimax: Getting over Indifference

    Publication Year: 2013 , Page(s): 79 - 86
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (824 KB) |  | HTML iconHTML  

    We propose Error Allowing Minimax, an algorithm resolving indifferences in the choices of pure minimax players in games of perfect information, to give the opponent the biggest possible target for errors. In contrast to the usual approach of defining a domain-specific static evaluation function with an infinite codomain, we achieve fine-grained positional evaluations by general considerations of t... View full abstract»

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  • Fast Strong Planning for FOND Problems with Multi-root Directed Acyclic Graphs

    Publication Year: 2013 , Page(s): 87 - 94
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (254 KB) |  | HTML iconHTML  

    We present a planner for addressing a difficult, yet under-investigated class of planning problems: Fully Observable Non-Deterministic planning problems with strong solutions. Our strong planner employs a new data structure, MRDAG (multi-root directed acyclic graph), to define how the solution space should be expanded. We further equip a MRDAG with heuristics to ensure planning towards the relevan... View full abstract»

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  • A Parallel Bottom-Up Resolution Algorithm Using Cilk

    Publication Year: 2013 , Page(s): 95 - 100
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (252 KB) |  | HTML iconHTML  

    Rapid developments of multicore processors in the last ten years have accelerated the advancements in concurrency platforms. Performance of bottom-up resolution algorithms used in logic programming and artificial intelligent systems, can potentially be improved using the parallel programming constructs offered by these platforms (e.g., OpenMP, Cilk++, etc.). In this work we use Cilk++ to implement... View full abstract»

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