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26th International Conference on Logic programming (ICLP)

作者： Drescher, Christian Walsh, Toby Vienna Univ Technol Vienna Austria Univ New S Wales Sydney NSW 2052 Australia Univ Nova Lisboa Lisbon Portugal

We present a new approach to enhancing answer set programming (ASP) with Constraint Processing techniques which allows for solving interesting Constraint Satisfaction Problems in ASP. We show how constraints on finite domains can be decomposed into logic programs such that unit-propagation achieves arc, bound or range consistency. Experiments with our encodings demonstrate their computational impact.

关键词： answer set programming constraint processing decomposition

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LEARNING DOMAIN-SPECIFIC HEURISTICS FOR answer set SOLVERS

LEARNING DOMAIN-SPECIFIC HEURISTICS FOR ANSWER SET SOLVERS

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26th International Conference on Logic programming (ICLP)

作者： Balduccini, Marcello Eastman Kodak Co Intelligent Syst KRL Rochester NY 14650 USA

ISBN: (纸本)9783939897170

In spite of the recent improvements in the performance of answer set programming (ASP) solvers, when the search space is sufficiently large, it is still possible for the search algorithm to mistakenly focus on areas of the search space that contain no solutions or very few. When that happens, performance degrades substantially, even to the point that the solver may need to be terminated before returning an answer. This prospect is a concern when one is considering using such a solver in an industrial setting, where users typically expect consistent performance. To overcome this problem, in this paper we propose a technique that allows learning domain-specific heuristics for ASP solvers. The learning is done off-line, on representative instances from the target domain, and the learned heuristics are then used for choice-point selection. In our experiments, the introduction of domain-specific heuristics improved performance on hard instances by up to 3 orders of magnitude (and 2 on average), nearly completely eliminating the cases in which the solver had to be terminated because the wait for an answer had become unacceptable.

关键词： answer set programming solvers domain-specific heuristics

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Testing and debugging techniques for answer set solver development

Testing and debugging techniques for answer set solver devel...

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26th International Conference on Logic programming (ICLP)

作者： Brummayer, Robert Jarvisalo, Matti Johannes Kepler Univ Linz Inst Formal Models & Verificat Linz Austria Univ Helsinki Dept Comp Sci FIN-00014 Helsinki Finland

This paper develops automated testing and debugging techniques for answer set solver development. We describe a flexible grammar-based black-box ASP fuzz testing tool which is able to reveal various defects such as unsound and incomplete behavior, i.e. invalid answer sets and inability to find existing solutions, in state-of-the-art answer set solver implementations. Moreover, we develop delta debugging techniques for shrinking failure-inducing inputs on which solvers exhibit defective behavior. In particular, we develop a delta debugging algorithm in the context of answer set solving, and evaluate two different elimination strategies for the algorithm.

关键词： answer set programming answer set solvers testing debugging

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DYNAMIC MAGIC setS FOR DISJUNCTIVE DATALOG PROGRAMS

DYNAMIC MAGIC SETS FOR DISJUNCTIVE DATALOG PROGRAMS

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26th International Conference on Logic programming (ICLP)

作者： Alviano, Mario Univ Calabria Dept Math I-87036 Arcavacata Di Rende CS Italy

ISBN: (纸本)9783939897170

answer set programming (ASP) is a powerful formalism for knowledge representation and common sense reasoning that allows disjunction in rule heads and non monotonic negation in bodies. Magic sets are a technique for optimizing query answering over logic programs and have been originally defined for standard Datalog, that is, ASP without disjunction and negation. Essentially, the input program is rewritten in order to identify a subset of the program instantiation which is sufficient for answering the query. Dynamic Magic sets (DMS) are an extension of this technique to ASP. The optimization provided by DMS can be exploited also during the nondeterministic phase of ASP systems. In particular, after some assumptions have been made during the computation, parts of the program may become irrelevant to a query (because of these assumptions). This allows for dynamic pruning of the search space, which may result in exponential performance gains. DMS has been implemented in the DLV system and experimental results confirm the effectiveness of the technique.

关键词： answer set programming decidability magic sets disjunctive logic programs

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Possibilistic uncertainty handling for answer set programming

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ANNALS OF MATHEMATICS AND ARTIFICIAL INTELLIGENCE 2006年第1-2期47卷 139-181页

作者： Nicolas, Pascal Garcia, Laurent Stephan, Igor Lefevre, Claire Univ Angers LERIA Angers France

In this work, we introduce a new framework able to deal with a reasoning that is at the same time non monotonic and uncertain. In order to take into account a certainty level associated to each piece of knowledge, we use possibility theory to extend the non monotonic semantics of stable models for logic programs with default negation. By means of a possibility distribution we define a clear semantics of such programs by introducing what is a possibilistic stable model. We also propose a syntactic process based on a fix-point operator to compute these particular models representing the deductions of the program and their certainty. Then, we show how this introduction of a certainty level on each rule of a program can be used in order to restore its consistency in case of the program has no model at all. Furthermore, we explain how we can compute possibilistic stable models by using available softwares for answer set programming and we describe the main lines of the system that we have developed to achieve this goal.

关键词： non monotonic reasoning uncertainty logic programming possibility theory answer set programming

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Mining and modelling interaction networks for systems biology

Mining and modelling interaction networks for systems biolog...

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作者： Fayruzov, Timur Ghent University

学位级别：master

The advances in biological research in the last two decades have provided many new insights on the functioning principles of living organisms. This resulted in the proposal of new methods to study the development of living organisms. One of the most significant advances is the development of microarrays and of high-throughput techniques that allow a biologist to collect a massive amount of experimental information about a particular biological process, which can be used for different purposes such as explanation of a living organism behaviour or drug design. The development of new experimental methods and rapid growth of biolog- ical data repositories have contributed to building biological network models that are aimed at reconstructing real biological processes in silico. On the other hand, advances in experimental techniques have led to a rapid increase of re- ports that describe new biological discoveries. Clearly, processing large amounts of information calls for automated methods that (partially) remove the burden of manual data processing from the shoulders of biologists. These two aspects of effective biological data utilization, namely automated text processing for information extraction and constructing and analyzing bio- logical network models, have attracted a lot of attention in computer science. In this thesis we have presented our contributions to these problems by studying both information extraction and modelling aspects. In the first part of the thesis, that comprises Chapters 2 and 3, we have dis- cussed the problem of information extraction from biological article abstracts, and in particular the problem of protein-protein interaction extraction. In Chap- ter 2 we have introduced the basic notions necessary to perform the interaction extraction task; we have discussed the natural language processing techniques and the machine learning setting for this task. Further, we have described the annotated corpora used for machine learning training and ev

关键词： Technology and Engineering Computer Science Bioinformatics Modelling Regulatory Networks Machine Learning answer set programming Text Mining

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Constructing Camin-Sokal phylogenies via answer set programming

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13th International Conference on Logic for programming, Artificial Intelligence, and Reasoning

作者： Kavanagh, Jonathan Mitchell, David Ternovska, Eugenia Manuch, Jan Zhao, Xiaohong Gupta, Arvind Simon Fraser Univ Burnaby BC V5A 1S6 Canada

ISBN: (纸本)3540482814

Constructing parsimonious phylogenetic trees from species data is a central problem in phylogenetics, and has diverse applications, even outside biology. Many variations of the problem, including the cladistic Camin-Sokal (CCS) version, are NP-complete. We present answer set programming (ASP) models for the binary CCS problem, as well as a simpler perfect phylogeny version, along with experimental results of applying the models to biological data. Our contribution is three-fold. First, we solve phylogeny problems which have not previously been tackled by ASP. Second, we report on variants of our CCS model which significantly. affect run time, including the interesting case of making the program "slightly tighter". This version exhibits some of the best performance, in contrast with a tight version of the model which exhibited poor performance. Third, we are able to find proven-optimal solutions for larger instances of the CCS problem than the widely used branch-and-bound-based PHYLIP package.

关键词： phylogeny maximum parsimony Camin-Sokal answer set programming

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FDNC: Decidable Nonmonotonic Disjunctive Logic Programs with Function Symbols

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ACM TRANSACTIONS ON COMPUTATIONAL LOGIC 2010年第2期11卷 14-14页

作者： Eiter, Thomas Simkus, Mantas Vienna Univ Technol Inst Informat Syst A-1040 Vienna Austria

We present the class FDNC of logic programs that allows for function symbols (F), disjunction (D), nonmonotonic negation under the answer set semantics (N), and constraints (C), while still retaining the decidability of the standard reasoning tasks. Thanks to these features, FDNC programs are a powerful formalism for rule-based modeling of applications with potentially infinite processes and objects, and which allows also for common-sense reasoning in this context. This is evidenced, for instance, by tasks in reasoning about actions and planning: brave and open queries over FDNC programs capture the well-known problems of plan existence and secure (conformant) plan existence, respectively, in transition-based actions domains. As for reasoning from FDNC programs, we show that consistency checking and brave/cautious reasoning tasks are EXPTIME-complete in general, but have lower complexity under syntactic restrictions that give rise to a family of program classes. Furthermore, we also determine the complexity of open queries (i.e., with answer variables), for which deciding non-empty answers is shown to be EXPSPACE-complete under cautious entailment. Furthermore, we present algorithms for all reasoning tasks that are worst-case optimal. The majority of them resorts to a finite representation of the stable models of an FDNC program that employs maximal founded sets of knots, which are labeled trees of depth at most 1 from which each stable model can be reconstructed. Due to this property, reasoning over FDNC programs can in many cases be reduced to reasoning from knots. Once the knot-representation for a program is derived (which can be done off-line), several reasoning tasks are not more expensive than in the function-free case, and some are even feasible in polynomial time. This knowledge compilation technique paves the way to potentially more efficient online reasoning methods not only for FDNC, but also for other formalisms.

关键词： Languages Theory answer set programming nonmonotonic logic programs function symbols computational complexity knowledge compilation reasoning about actions description logics

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An Inverse Lambda Calculus Algorithm For Natural Language Processing

An Inverse Lambda Calculus Algorithm For Natural Language Pr...

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作者： Alvarez Gonzalez, Marcos Arizona State University

学位级别：M.S.

Natural Language Processing is a subject that combines computer science and linguistics, aiming to provide computers with the ability to understand natural language and to develop a more intuitive human-computer interaction. The research community has developed ways to translate natural language to mathematical formalisms. It has not yet been shown, however, how to automatically translate different kinds of knowledge in English to distinct formal languages. Most of the recent work presents the problem that the translation method aims to a specific formal language or is hard to generalize. In this research, I take a first step to overcome this difficulty and present two algorithms which take as input two lambda-calculus expressions G and H and compute a lambda-calculus expression F. The expression F returned by the first algorithm satisfies F@G=H and, in the case of the second algorithm, we obtain G@F=H. The lambda expressions represent the meanings of words and sentences. For each formal language that one desires to use with the algorithms, the language must be defined in terms of lambda calculus. Also, some additional concepts must be included. After doing this, given a sentence, its representation and knowing the representation of several words in the sentence, the algorithms can be used to obtain the representation of the other words in that sentence. In this work, I define two languages and show examples of their use with the algorithms. The algorithms are illustrated along with soundness and completeness proofs, the latter with respect to typed lambda-calculus formulas up to the second order. These algorithms are a core part of a natural language semantics system that translates sentences from English to formulas in different formal languages.

关键词： Computer Science Artificial Intelligence answer set programming CCG first order logic inverse knowledge representation lambda calculus

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Modular Nonmonotonic Logic programming Revisited

Modular Nonmonotonic Logic Programming Revisited

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25th International Conference on Logic programming (ICLP)

作者： Dao-Tran, Minh Eiter, Thomas Fink, Michael Krennwallner, Thomas Vienna Univ Technol Inst Informat Syst A-1040 Vienna Austria

ISBN: (纸本)9783642028458

Recently, enabling modularity aspects in answer set programming (ASP) has gained increasing interest to ease the composition of program parts to an overall program. In this paper, we focus on modular nonmonotonic logic programs (MLP) under the answer set semantics, whose modules may have contextually dependent input provided by other modules. Moreover, (Mutually) recursive module calls are allowed. We define a model-theoretic semantics for this extended setting, show that many desired properties of ordinary logic programming generalize to our modular ASP, and determine the computational complexity of the new formalism. We investigate the relationship of modular programs to disjunctive logic programs with well-defined input/output interface (DLP-functions) and show that they can be embedded into MLPs.

关键词： Knowledge Representation answer set programming Modular Logic programming

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