Weak and strong disjunction in possibilistic ASP

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

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

6 Citations (Scopus)
177 Downloads (Pure)

Abstract

Possibilistic answer set programming (PASP) unites answer set programming (ASP) and possibilistic logic (PL) by associating certainty values with rules. The resulting framework allows to combine both non-monotonic reasoning and reasoning under uncertainty in a single framework. While PASP has been well-studied for possibilistic definite and possibilistic normal programs, we argue that the current semantics of possibilistic disjunctive programs are not entirely satisfactory. The problem is twofold. First, the treatment of negation-as-failure in existing approaches follows an all-or-nothing scheme that is hard to match with the graded notion of proof underlying PASP. Second, we advocate that the notion of disjunction can be interpreted in several ways. In particular, in addition to the view of ordinary ASP where disjunctions are used to induce a non-deterministic choice, the possibilistic setting naturally leads to a more epistemic view of disjunction. In this paper, we propose a semantics for possibilistic disjunctive programs, discussing both views on disjunction. Extending our earlier work, we interpret such programs as sets of constraints on possibility distributions, whose least specific solutions correspond to answer sets.
Original languageEnglish
Title of host publicationProceedings of the 5th International Conference on Scalable Uncertainty Management (SUM)
EditorsSalem Benferhat, John Grant
PublisherSpringer-Verlag
Pages475-488
Number of pages14
DOIs
Publication statusPublished - 2011

Publication series

NameLecture Notes in Artificial Intelligence
PublisherSpringer-Verlag
Volume6929
ISSN (Print)0302-9743

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    Bauters, K., Schockaert, S., Cock, M. D., & Vermeir, D. (2011). Weak and strong disjunction in possibilistic ASP. In S. Benferhat, & J. Grant (Eds.), Proceedings of the 5th International Conference on Scalable Uncertainty Management (SUM) (pp. 475-488). (Lecture Notes in Artificial Intelligence; Vol. 6929). Springer-Verlag. https://doi.org/10.1007/978-3-642-23963-2_37