article

Logic programming and knowledge representation-the A-prolog perspective

Artificial Intelligence, Volume 138, Issue 1-2

Pages 3 - 38

https://doi.org/10.1016/S0004-3702(02)00207-2

Published: 01 June 2002 Publication History

Abstract

In this paper we give a short introduction to logic programming approach to knowledge representation and reasoning. The intention is to help the reader to develop a `feel' for the field's history and some of its recent developments. The discussion is mainly limited to logic programs under the answer set semantics. For understanding of approaches to logic programming built on well-founded semantics, general theories of argumentation, abductive reasoning, etc., the reader is referred to other publications.

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Index Terms

Logic programming and knowledge representation-the A-prolog perspective
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
      1. Logic programming and answer set programming
      2. Nonmonotonic, default reasoning and belief revision
2. Theory of computation
  1. Logic
    1. Constraint and logic programming

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We first embed Pearce's equilibrium logic and Ferraris's propositional general logic programs in Lin and Shoham's logic of GK, a nonmonotonic modal logic that has been shown to include as special cases both Reiter's default logic in the propositional ...
‘Classical’ Negation in Nonmonotonic Reasoning and Logic Programming

Gelfond and Lifschitz were the first to point out the need for a symmetric negation in logic programming and they also proposed a specific semantics for such negation for logic programs with the stable semantics, which they called ‘classical’. Subsequently,...
Ground Nonmonotonic Modal Logic S5: New Results

We study logic programs under Gelfond's translation in the context of modal logic S5. We show that for arbitrary logic programs (propositional theories where logic negation is associated with default negation) ground nonmonotonic modal logics between T ...

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Reviewer: Matthew Mark Huntbach

Classic Prolog, with its SLD-resolution method for answering queries, has stood the test of time, remaining the only well-known logic programming language, in spite of many proposed alternatives. Prolog’s logic is severely restricted, but it is enhanced with “negation as failure” (NAF), in which the negation of a query is assumed true if an attempt to answer the query using SLD-resolution fails. Thus, NAF makes a closed world assumption: there is nothing beyond the world we know about. This paper summarizes recent work, and establishes a new basis for logic programming using answer set semantics rather than SLD-resolution. This enables a much richer handling of negation than in Prolog. Non-monotonicity is accepted and handled well, rather than introduced accidentally, as in Prolog’s NAF. There is a neat handling of defaults: common-sense reasoning we use in real life, since we don’t live in closed worlds. The possibility of the practical use of answer set semantic logic programming is increased by the development of more efficient reasoning algorithms for it, one of which is described in this paper. The emphasis of the paper, however, is much more on getting the logic right than on practical programming language implementation. Logic programming has dropped out of fashion since the boom associated with its adoption by the Japanese Fifth Generation project. This paper shows that it has not stood still, and that some previously tricky issues have now been tackled. Online Computing Reviews Service

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Published In

Artificial Intelligence Volume 138, Issue 1-2

June 2002

231 pages

ISSN:0004-3702

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Elsevier Science Publishers Ltd.

United Kingdom

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Published: 01 June 2002

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Martin É(2022)Disjunctive logic programs, answer sets, and the cut ruleArchive for Mathematical Logic10.1007/s00153-022-00821-x61:7-8(903-937)Online publication date: 1-Nov-2022
https://dl.acm.org/doi/10.1007/s00153-022-00821-x
Faber W(2020)An Introduction to Answer Set Programming and Some of Its ExtensionsReasoning Web. Declarative Artificial Intelligence10.1007/978-3-030-60067-9_6(149-185)Online publication date: 24-Jun-2020
https://dl.acm.org/doi/10.1007/978-3-030-60067-9_6
Sun XZhang Y(2019)A Review of Domain Knowledge Representation for Robot Task PlanningProceedings of the 2019 4th International Conference on Mathematics and Artificial Intelligence10.1145/3325730.3325756(176-183)Online publication date: 12-Apr-2019
https://dl.acm.org/doi/10.1145/3325730.3325756
Ulbricht MThimm MBrewka GMcIlraith SWeinberger K(2018)Measuring strong inconsistencyProceedings of the Thirty-Second AAAI Conference on Artificial Intelligence and Thirtieth Innovative Applications of Artificial Intelligence Conference and Eighth AAAI Symposium on Educational Advances in Artificial Intelligence10.5555/3504035.3504277(1989-1996)Online publication date: 2-Feb-2018
https://dl.acm.org/doi/10.5555/3504035.3504277
Hunter AThimm M(2017)Probabilistic reasoning with abstract argumentation frameworksJournal of Artificial Intelligence Research10.5555/3176788.317680259:1(565-611)Online publication date: 1-May-2017
https://dl.acm.org/doi/10.5555/3176788.3176802
Potyka NThimm M(2017)Inconsistency-tolerant reasoning over linear probabilistic knowledge basesInternational Journal of Approximate Reasoning10.1016/j.ijar.2017.06.00288:C(209-236)Online publication date: 1-Sep-2017
https://dl.acm.org/doi/10.1016/j.ijar.2017.06.002
Fuscà DGermano SZangari JAnastasio MCalimeri FPerri SCheney JVidal G(2016)A framework for easing the development of applications embedding answer set programmingProceedings of the 18th International Symposium on Principles and Practice of Declarative Programming10.1145/2967973.2968594(38-49)Online publication date: 5-Sep-2016
https://dl.acm.org/doi/10.1145/2967973.2968594
Dung P(2016)An axiomatic analysis of structured argumentation with prioritiesArtificial Intelligence10.1016/j.artint.2015.10.005231:C(107-150)Online publication date: 1-Feb-2016
https://dl.acm.org/doi/10.1016/j.artint.2015.10.005
Calimeri FGebser MMaratea MRicca F(2016)Design and results of the Fifth Answer Set Programming CompetitionArtificial Intelligence10.1016/j.artint.2015.09.008231:C(151-181)Online publication date: 1-Feb-2016
https://dl.acm.org/doi/10.1016/j.artint.2015.09.008
Marra GUrsino DRicca FTerracina G(2016)Information diffusion in a multi-social-network scenarioKnowledge and Information Systems10.1007/s10115-015-0890-z48:3(619-648)Online publication date: 1-Sep-2016
https://dl.acm.org/doi/10.1007/s10115-015-0890-z
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From answer set logic programming to circumscription via logic of GK

‘Classical’ Negation in Nonmonotonic Reasoning and Logic Programming

Ground Nonmonotonic Modal Logic S5: New Results

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