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16th International Conference on logic programming and Non-Monotonic Reasoning (LPNMR)

作者： Trieu, Ly Ly Son, Tran Cao Balduccini, Marcello New Mexico State Univ Las Cruces NM 88003 USA St Josephs Univ Philadelphia PA 19131 USA

ISBN: (纸本)9783031157073;9783031157066

In this paper, we present a system, called xASP, for generating explanations that explain why an atom belongs to (or does not belong to) an answer set of a given program. The system can generate all possible explanations for a query without the need to simplify the program before computing explanations, i.e., it works with non-ground programs. These properties distinguish xASP from existing systems such as xClingo, DiscASP, exp(ASP(c)), and s(CASP), which also generate explanations for queries to logic programs under the answer set semantics but simplify and ground the programs (the three systems xClingo, DiscASP, exp(ASP(c))) or do not always generate all possible explanations (the system s(CASP)). In addition, the output of xASP is insensitive to syntactic variations such as the order conditions and the order of rules, which is also different from the output of s(CASP).

关键词： Explainable AI logic programming Answer Set programming

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Determining Action Reversibility in STRIPS Using Answer Set and Epistemic logic programming

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theory and practice of logic programming 2021年第5期21卷 646-662页

作者： Faber, Wolfgang Morak, Michael Chrpa, Lukas Univ Klagenfurt Klagenfurt Austria Czech Tech Univ Prague Czech Republic

In the context of planning and reasoning about actions and change, we call an action reversible when its effects can be reverted by applying other actions, returning to the original state. Renewed interest in this area has led to several results in the context of the PDDL language, widely used for describing planning tasks. In this paper, we propose several solutions to the computational problem of deciding the reversibility of an action. In particular, we leverage an existing translation from PDDL to Answer Set programming (ASP), and then use several different encodings to tackle the problem of action reversibility for the STRIPS fragment of PDDL. For these, we use ASP, as well as Epistemic logic programming (ELP), an extension of ASP with epistemic operators, and compare and contrast their strengths and weaknesses.

关键词： action reversibility Answer Set programming Epistemic logic programming reasoning about action and change

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Split Decisions: Explicit Contexts for Substructural Languages 25

Split Decisions: Explicit Contexts for Substructural Languag...

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14th Conference on Certified Programs and Proofs

作者： Zackon, Daniel Sano, Chuta Momigliano, Alberto Pientka, Brigitte McGill Univ Montreal PQ Canada Univ Milan Milan Italy

ISBN: (纸本)9798400713477

A central challenge in mechanizing the meta-theory of substructural languages is modeling contexts. Although various ad hoc approaches to this problem exist, we lack a set of good practices and a simple infrastructure that can be leveraged for mechanizing a wide range of substructural systems. In this work, we describe Contexts as Resource Vectors (CARVe), a general syntactic infrastructure for managing substructural contexts, where elements are annotated with tags from a resource algebra denoting their availability. Assumptions persist as contexts are manipulated since we model resource consumption by changing their tags. We may thus define relations between substructural contexts via simultaneous substitutions without the need to split them. Moreover, we establish a series of algebraic properties about context operations that are typically required to carry out proofs in practice. CARVe is implemented in the proof assistant Beluga. To illustrate best practices for using our infrastructure, we give a detailed reformulation of the linear sequent calculus and bidirectional linear lambda-calculus in terms of CARVe's context operations and prove their equivalence using the aforementioned algebraic properties. In addition, we apply CARVe to mechanize a diverse set of systems, from the affine lambda-calculus to the session-typed process calculus CP, giving us confidence that CARVe is sufficiently general to mechanize a broad range of substructural systems.

关键词： mechanized meta-theory substructural type systems substructural logics linear logic verification

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Learning to Rank the Distinctiveness of Behaviour in Serial Offending 16th

Learning to Rank the Distinctiveness of Behaviour in Serial ...

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16th International Conference on logic programming and Non-Monotonic Reasoning (LPNMR)

作者： Law, Mark Sautory, Theophile Mitchener, Ludovico Davies, Kari Tonkin, Matthew Woodhams, Jessica Alrajeh, Dalal ILASP Ltd Grantham England Univ Calif Berkeley Berkeley CA 94720 USA Imperial Coll London London England Univ Bournemouth Bournemouth Dorset England Univ Leicester Leicester Leics England Univ Birmingham Birmingham W Midlands England

ISBN: (纸本)9783031157073;9783031157066

Comparative Case Analysis is an analytical process used to detect serial offending. It focuses on identifying distinctive behaviour that an offender displays consistently when committing their crimes. In practice, crime analysts consider the context in which each behaviour occurs to determine its distinctiveness, which subsequently impacts on their determination of whether crimes are committed by the same person or not. Existing algorithms do not currently consider context in this way when generating linkage predictions. This paper presents the first learning-based approach aimed at identifying contexts within which behaviour may be considered more distinctive. We show how this problem can be modelled as that of learning preferences (in answer set programming) from examples of ordered pairs of contexts in which a behaviour was observed. In this setting, a context is preferred to another context if the behaviour is rarer in the first context. We make novel use of odds ratios to determine which examples are used for learning. Our approach has been applied to a real dataset of serious sexual offences provided by the UK National Crime Agency. The approach provides (i) a systematic methodology for selecting examples from which to learn preferences;(ii) novel insights for practitioners into the contexts under which an exhibited behaviour is more rare.

关键词： Crime Linkage Answer Set programming Inductive logic programming

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Exploring the Jungle of Intuitionistic Temporal logics

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theory and practice of logic programming 2021年第4期21卷 459-492页

作者： Boudou, Joseph Dieguez, Martin Fernandez-Duque, David Kremer, Philip Toulouse Univ IRIT Toulouse France Univ Angers LERIA Angers France Univ Ghent Dept Math WE16 Ghent Belgium Univ Toronto Dept Philosophy Toronto ON Canada

The importance of intuitionistic temporal logics in Computer Science and Artificial Intelligence has become increasingly clear in the last few years. From the proof-theory point of view, intuitionistic temporal logics have made it possible to extend functional programming languages with new features via type theory, while from the semantics perspective, several logics for reasoning about dynamical systems and several semantics for logic programming have their roots in this framework. We consider several axiomatic systems for intuitionistic linear temporal logic and show that each of these systems is sound for a class of structures based either on Kripke frames or on dynamic topological systems. We provide two distinct interpretations of "henceforth", both of which are natural intuitionistic variants of the classical one. We completely establish the order relation between the semantically defined logics based on both interpretations of "henceforth" and, using our soundness results, show that the axiomatically defined logics enjoy the same order relations.

关键词： theory Knowledge Representation and Nonmonotonic Reasoning

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A Machine Learning System to Improve the Performance of ASP Solving Based on Encoding Selection 16th

A Machine Learning System to Improve the Performance of ASP ...

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16th International Conference on logic programming and Non-Monotonic Reasoning (LPNMR)

作者： Liu, Liu Truszczynski, Mirek Lierler, Yuliya Univ Kentucky Lexington KY 40506 USA Univ Nebraska Omaha NE 68182 USA

ISBN: (纸本)9783031157073;9783031157066

Answer set programming (ASP) has long been used for modeling and solving hard search problems. Experience shows that the performance of ASP tools on different ASP encodings of the same problem may vary greatly from instance to instance and it is rarely the case that one encoding outperforms all others. We describe a system and its implementation that given a set of encodings and a training set of instances, builds performance models for the encodings, predicts the execution time of these encodings on new instances, and uses these predictions to select an encoding for solving.

关键词： answer set programming encoding selection machine learning

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A Practical Account into Counting Dung's Extensions by Dynamic programming 16th

A Practical Account into Counting Dung's Extensions by Dynam...

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16th International Conference on logic programming and Non-Monotonic Reasoning (LPNMR)

作者： Dewoprabowo, Ridhwan Fichte, Johannes Klaus Gorczyca, Piotr Jerzy Hecher, Markus Tech Univ Dresden Dresden Germany TU Wien Vienna Austria

ISBN: (纸本)9783031157073;9783031157066

A Abstract argumentation and Dung's framework are popular for modeling and evaluating arguments in artificial intelligence. We consider various counting problems in abstract argumentation under practical aspects. We revisit algorithms and establish a framework that employs dynamic programming on tree decompositions for counting extensions of abstract argumentation frameworks under admissible, stable, and complete semantics. We provide an empirical evaluation and investigate conditions under which our approach is useful.

关键词： Dynamic programming

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Building Information Modeling Using Constraint logic programming

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theory and practice of logic programming 2022年第5期22卷 723-738页

作者： Arias, Joaquin Torma, Seppo Carro, Manuel Gupta, Gopal Univ Rey Juan Carlos CETINIA Madrid Spain VisuaLynk Oy Espoo Finland Univ Politecn Madrid Madrid Spain IMDEA Software Inst Pozuelo De Alarcon Spain Univ Texas Dallas Richardson TX 75080 USA

Building Information Modeling (BIM) produces three-dimensional object-oriented models of buildings combining the geometrical information with a wide range of properties about materials, products, safety, to name just a few. BIM is slowly but inevitably revolutionizing the architecture, engineering, and construction industry. Buildings need to be compliant with regulations about stability, safety, and environmental impact. Manual compliance checking is tedious and error-prone, and amending flaws discovered only at construction time causes huge additional costs and delays. Several tools can check BIM models for conformance with rules/guidelines. For example, Singapore's CORENET e-Submission System checks fire safety. But since the current BIM exchange format only contains basic information about building objects, a separate, ad-hoc model pre-processing is required to determine, for example, evacuation routes. Moreover, they face difficulties in adapting existing built-in rules and/or adding new ones (to cater for building regulations, that can vary not only among countries but also among parts of the same city), if at all possible. We propose the use of logic-based executable formalisms (CLP and Constraint ASP) to couple BIM models with advanced knowledge representation and reasoning capabilities. Previous experience shows that such formalisms can be used to uniformly capture and reason with knowledge (including ambiguity) in a large variety of domains. Additionally, incorporating checking within design tools makes it possible to ensure that models are rule-compliant at every step. This also prevents erroneous designs from having to be (partially) redone, which is also costly and burdensome. To validate our proposal, we implemented a preliminary reasoner under CLP(Q/R) and ASP with constraints and evaluated it with several BIM models.

关键词： building information modeling constraint commonsense reasoning answer set programming

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Nonground Abductive logic programming with Probabilistic Integrity Constraints

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theory and practice of logic programming 2021年第5期21卷 557-574页

作者： Bellodi, Elena Gavanelli, Marco Zese, Riccardo Lamma, Evelina Riguzzi, Fabrizio Univ Ferrara Dept Engn Ferrara Italy Univ Ferrara Dept Math & Comp Sci Ferrara Italy

Uncertain information is being taken into account in an increasing number of application fields. In the meantime, abduction has been proved a powerful tool for handling hypothetical reasoning and incomplete knowledge. Probabilistic logical models are a suitable framework to handle uncertain information, and in the last decade many probabilistic logical languages have been proposed, as well as inference and learning systems for them. In the realm of Abductive logic programming (ALP), a variety of proof procedures have been defined as well. In this paper, we consider a richer logic language, coping with probabilistic abduction with variables. In particular, we consider an ALP program enriched with integrity constraints a la IFF, possibly annotated with a probability value. We first present the overall abductive language and its semantics according to the Distribution Semantics. We then introduce a proof procedure, obtained by extending one previously presented, and prove its soundness and completeness.

关键词： abduction integrity constraints distribution semantics probabilistic IFF

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Historical Review of Variants of Informal Semantics for logic Programs under Answer Set Semantics: GL’88, GL’91, GK’14, D-V’12

arXiv

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arXiv 2024年

作者： Lierler, Yuliya University of Nebraska Omaha United States

This note presents a historical survey of informal semantics that are associated with logic programming under answer set semantics. We review these in uniform terms and align them with two paradigms: Answer Set programming and ASP-Prolog— two prominent Knowledge Representation and Reasoning Paradigms in Artificial Intelligence. Under consideration in theory and practice of logic programming (TPLP). © 2024, CC BY.

关键词： logic programming

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