Possibilistic Answer Set Programming Revisited

Kim Bauters, Steven Schockaert, Martine De Cock, Dirk Vermeir

Research output: Chapter in Book/Report/Conference proceedingConference contribution

25 Citations (Scopus)

Abstract

Possibilistic answer set programming (PASP) extends answer set programming (ASP) by attaching to each rule a degree of certainty. While such an extension is important from an application point of view, existing semantics are not well-motivated, and do not always yield intuitive results. To develop a more suitable semantics, we first introduce a characterization of answer sets of classical ASP programs in terms of possibilistic logic where an ASP program specifies a set of constraints on possibility distributions. This characterization is then naturally generalized to define answer sets of PASP programs. We furthermore provide a syntactic counterpart, leading to a possibilistic generalization of the well-known Gelfond-Lifschitz reduct, and we show how our framework can readily be implemented using standard ASP solvers.
Original languageEnglish
Title of host publicationProceedings of the 26th Conference on Uncertainty in Artificial Intelligence (UAI 2010)
EditorsPeter Grünwald, Peter Spirtes
PublisherAUAI Press
Number of pages8
ISBN (Print)9780974903965
Publication statusPublished - 2010
EventThe 26th Conference on Uncertainty in Artificial Intelligence (UAI 2010) - Catalina Island, California, United States
Duration: 08 Jul 201011 Jul 2010

Conference

ConferenceThe 26th Conference on Uncertainty in Artificial Intelligence (UAI 2010)
CountryUnited States
CityCalifornia
Period08/07/201011/07/2010

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