Uncertainty in Artificial Intelligence
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Possibilistic Answer Set Programming Revisited
Kim Bauters, Steven Schockaert, Martine De Cock, Dirk Vermeir
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.
Keywords:
Pages: 48-55
PS Link:
PDF Link: /papers/10/p48-bauters.pdf
BibTex:
@INPROCEEDINGS{Bauters10,
AUTHOR = "Kim Bauters and Steven Schockaert and Martine De Cock and Dirk Vermeir",
TITLE = "Possibilistic Answer Set Programming Revisited",
BOOKTITLE = "Proceedings of the Twenty-Sixth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-10)",
PUBLISHER = "AUAI Press",
ADDRESS = "Corvallis, Oregon",
YEAR = "2010",
PAGES = "48--55"
}


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