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17th international conference on Principles of Knowledge Representation and reasoning, KR 2020

作者： Gogacz, Tomasz Lukumbuzya, Sanja Ortiz, Magdalena Šimkus, Mantas Institute of Informatics University of Warsaw Poland Institute of Logic and Computation TU Wien Austria

ISBN: (纸本)9781713825982

We study the relative expressiveness of ontology-mediated queries (OMQs) formulated in the expressive Description logic ALCHOIF extended with closed predicates. In particular, we present a polynomial-time translation from OMQs into Datalog with negation under the stable model semantics, the formalism that underlies Answer Set programming. this is a novel and non-trivial result: the considered OMQs are not only non-monotonic but also feature a tricky combination of nominals, inverse roles, and role functionality. We start with atomic queries and then lift our approach to a large class of first-order queries where quantification is "guarded" by closed predicates. Our translation is based on a characterization of the query answering problem via integer programming, and a specially crafted program in Datalog with negation that finds solutions to dynamically generated systems of integer inequalities. As an important by-product of our translation, we get that the query answering problem is co-NP-complete in data complexity for the considered class of OMQs. thus, answering these OMQs in the presence of closed predicates is not harder than answering them in the standard setting. this is not obvious as closed predicates are known to increase data complexity for some existing ontology languages. © 2020 17th international conference on Principles of Knowledge Representation and reasoning, KR 2020. All rights reserved.

关键词： Integer programming

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Preference logic Grammars: Fixed point semantics and application to data standardization

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ARTIFICIAL INTELLIGENCE 2002年第1-2期138卷 117-147页

作者： Cui, BQ Swift, T IBM Corp Silicon Valley Lab San Jose CA 95123 USA SUNY Stony Brook Dept Comp Sci Stony Brook NY 11794 USA

the addition of preferences to normal logic pro-rams is a convenient way to represent many aspects of default reasoning. If the derivation of an atom A(1) is preferred to that of an atom A(2) a preference rule can be defined so that A(2) is derived only if A(1) is not. Although such situations can be modelled directly using default negation, it is often easier to define preference rules than it is to add negation to the bodies of rules. As first noted by Govindarajan et al. [Proc. Internat. Conf. on logic programming, 1995, pp. 731-746], for certain grammars, it may be easier to disambiguate parses using preferences than by enforcing disambiguation in the grammar rules themselves. In this paper we define a general fixed-point semantics for preference logic programs based on an embedding into the well-founded semantics, and discuss its features and relation to previous preference logic semantics. We then study how preference logic grammars are used in data standardization, the commercially important process of extracting useful information from poorly structured textual data. this process includes correcting misspellings and truncations that occur in data, extraction of relevant C, 41 information via parsing, and correcting inconsistencies in the extracted information. the declarativity of Prolog offers natural advantages for data standardization, and a commercial standardizer has been implemented using Prolog. However, we show that the use of preference logic grammars allow construction of a much more powerful and declarative commercial standardizer, and discuss in detail how the use of the non-monotonic construct of preferences leads to improved commercial software. (C) 2002 Elsevier Science B.V. All rights reserved.

关键词： logic programming non-monotonic reasoning reasoning with preferences XSB natural language processing

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From logic programming and non-monotonic reasoning to Computational Argumentation and Beyond 14th

From Logic Programming and Non-monotonic Reasoning to Comput...

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14th international conference on logic programming and nonmonotonic reasoning (LPNMR)

作者： Toni, Francesca Imperial Coll London London England

ISBN: (纸本)9783319616605;9783319616599

Argumentation has gained popularity in AI in recent years to support several activities and forms of reasoning. this talk will trace back the logic programming and non-monotonic reasoning origins of two well-known argumentation formalisms in AI (namely abstract argumentation and assumption-based argumentation). Finally, the talk will discuss recent developments in AI making use of computational argumentation, in particular to support collaborative decision making.

关键词： logic programming

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the Design of the Seventh Answer Set programming Competition 14th

The Design of the Seventh Answer Set Programming Competition

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14th international conference on logic programming and nonmonotonic reasoning (LPNMR)

作者： Gebser, Martin Maratea, Marco Ricca, Francesco Univ Potsdam Inst Comp Sci Potsdam Germany Univ Genoa DIBRIS Genoa Italy Univ Calabria Dipartimento Matemat & Informat Arcavacata Di Rende Italy

ISBN: (纸本)9783319616605;9783319616599

Answer Set programming (ASP) is a prominent knowledge representation language with roots in logic programming and non-monotonic reasoning. Biennial competitions are organized in order to furnish challenging benchmark collections and assess the advancement of the state of the art in ASP solving. In this paper, we report about the design of the Seventh ASP Competition, which is jointly organized by the University of Calabria (Italy), the University of Genova (Italy), and the University of Potsdam (Germany), in affiliation with the 14th international conference on logic programming and non-monotonic reasoning (LPNMR 2017). A novel feature of this competition edition is the re-introduction of a Model&Solve track, complementing the usual System track with problem domains where participants need to provide dedicated encodings and solving means.

关键词： logic programming

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A new algorithm to automate inductive learning of default theories

A new algorithm to automate inductive learning of default th...

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33rd international conference on logic programming colocated with the 23rd international conference on Principles and Practice of Constraint programming / 20th international conference on theory and Applications of Satisfiability Testing

作者： Shakerin, Farhad Salazar, Elmer Gupta, Gopal Univ Texas Dallas Richardson TX 75083 USA

In inductive learning of a broad concept, an algorithm should be able to distinguish concept examples from exceptions and noisy data. An approach through recursively finding patterns in exceptions turns out to correspond to the problem of learning default theories. Default logic is what humans employ in common-sense reasoning. therefore, learned default theories are better understood by humans. In this paper, we present new algorithms to learn default theories in the form of non-monotonic logic programs. Experiments reported in this paper show that our algorithms are a significant improvement over traditional approaches based on inductive logic programming. Under consideration for acceptance in TPLP.

关键词： inductive logic programming non-monotonic logic programming default reasoning common-sense reasoning machine learning

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A logic programming framework for possibilistic argumentation:: Formalization and logical properties

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FUZZY SETS AND SYSTEMS 2008年第10期159卷 1208-1228页

作者： Alsinet, Teresa Chesnevar, Carlos I. Godo, Lluis Simari, Guillermo R. Univ Lleida Dept Comp Sci Lleida 6925001 Spain UAB Artificial Intelligence Res Inst IIIA Bellaterra Spain Univ Nacl Sur Dept Comp Sci & Engn RA-8000 Bahia Blanca Buenos Aires Argentina

In the last decade defeasible argumentation frameworks have evolved to become a sound setting to formalize commonsense, qualitative reasoning. the logic programming paradigm has shown to be particularly useful for developing different argument-based frameworks on the basis of different variants of logic programming which incorporate defeasible rules. Most of such frameworks, however, are unable to deal with both explicit uncertainty and vague knowledge, as defeasibility is directly encoded in the object language. this paper presents possibilistic defeasible logic programming (P-DeLP), a new logic programming language which combines features from argumentation theory and logic programming, incorporating as well the treatment of possibilistic uncertainty. Such features are formalized on the basis of PGL, a possibilistic logic based on Godel fuzzy logic. One of the applications of P-DeLP is providing an intelligent agent with non-monotonic, argumentative inference capabilities. In this paper we also provide a better understanding of such capabilities by defining two non-monotonic operators which model the expansion of a given program by adding new weighed facts associated with argument conclusions and warranted literals, respectively. Different logical properties for the proposed operators are studied. (c) 2008 Elsevier B.V. All rights reserved.

关键词： possibilistic logic vague knowledge defeasible argumentation intelligent systems

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Belief, knowledge, revisions, and a semantics of non-monotonic reasoning 5th

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5th international conference on logic programming and nonmonotonic reasoning (LPNMR 99)

作者： Sefránek, J Comenius Univ Inst Informat Bratislava 84215 Slovakia

ISBN: (纸本)3540667490

Przymusinski's Autoepistemic logic of Knowledge and Belief (AELKB) is a unifying framework for various non-monotonic formalisms. In this paper we present a semantic characterization of AELKB in terms of Dynamic Kripke Structures (DKS). A DKS is composed of two components - a static one (a Kripke structure) and a dynamic one (a set of transformations). Transformations between possible worlds correspond to hypotheses generation and to revisions. therefore they enable to define a semantics of insertions to and revisions of AELKB-theories. A computation of the transformations (between possible worlds) is based on (an enhanced) model-checking. the transformations may be used as a method of computing static autoepistemic expansions.

关键词： non-monotonic reasoning autoepistemic logic of knowledge and belief dynamic Kripke structure belief revision model checking

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Hybrid Probabilistic logic Programs with non-monotonic negation

Hybrid Probabilistic Logic Programs with non-monotonic negat...

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21st international conference on logic programming

作者： Saad, E Pontelli, E New Mexico State Univ Dept Comp Sci Las Cruces NM 88003 USA

ISBN: (纸本)354029208X

In [22], a new Hybrid Probabilistic logic Programs framework has been proposed, and a new semantics has been developed to enable encoding and reasoning about real-world applications. In this paper, the language of Hybrid Probabilistic logic Programs framework of [22] is extended to allow non-monotonic negation, and two alternative semantics are defined: stable probabilistic model semantics and probabilistic well-founded semantics. Stable probabilistic model semantics and probabilistic well-founded semantics generalize stable model semantics and well-founded semantics of traditional normal logic programs, and they reduce to the semantics of original Hybrid Probabilistic logic programs framework of [22] for programs without negation, It is the first time that two different semantics for Hybrid Probabilistic Programs with non-monotonic negation as well as their relationships are described. this development provides a foundational ground for developing computational methods for computing the proposed semantics. Furthermore, it makes it clearer how to characterize non-monotonic negation in probabilistic logic programming frameworks for commonsense reasoning.

关键词： logic programming

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Paracoherent Answer Set Semantics meets Argumentation Frameworks

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thEORY AND PRACTICE OF logic programming 2019年第5-6期19卷 688-704页

作者： Amendola, Giovanni Ricca, Francesco Univ Calabria Arcavacata Di Rende Italy

In the last years, abstract argumentation has met with great success in AI, since it has served to capture several non-monotonic logics for AI. Relations between argumentation framework (AF) semantics and logic programming ones are investigating more and more. In particular, great attention has been given to the well-known stable extensions of an AF, that are closely related to the answer sets of a logic program. However, if a framework admits a small incoherent part, no stable extension can be provided. To overcome this shortcoming, two semantics generalizing stable extensions have been studied, namely semi-stable and stage. In this paper, we show that another perspective is possible on incoherent AFs, called paracoherent extensions, as they have a counterpart in paracoherent answer set semantics. We compare this perspective with semi-stable and stage semantics, by showing that computational costs remain unchanged, and moreover an interesting symmetric behaviour is maintained.

关键词： Answer Set programming Abstract Argumentation Paracoherent reasoning

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Learning to assign degrees of belief in relational domains

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MACHINE LEARNING 2008年第1期73卷 25-53页

作者： Koriche, Frederic Univ Montpellier 2 LIRMM F-34392 Montpellier 5 France

A recurrent problem in the development of reasoning agents is how to assign degrees of beliefs to uncertain events in a complex environment. the standard knowledge representation framework imposes a sharp separation between learning and reasoning;the agent starts by acquiring a "model" of its environment, represented into an expressive language, and then uses this model to quantify the likelihood of various queries. Yet, even for simple queries, the problem of evaluating probabilities from a general purpose representation is computationally prohibitive. In contrast, this study embarks on the learning to reason (L2R) framework that aims at eliciting degrees of belief in an inductive manner. the agent is viewed as an anytime reasoner that iteratively improves its performance in light of the knowledge induced from its mistakes. Indeed, by coupling exponentiated gradient strategies in learning and weighted model counting techniques in reasoning, the L2R framework is shown to provide efficient solutions to relational probabilistic reasoning problems that are provably intractable in the classical paradigm.

关键词： learning to reason online learning relational probabilistic reasoning exponentiated gradient learning Markov networks weighted model counting

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