Possibilistic Inferences in Answer Set Programming

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Jin, Yifan
Wang, Kewen
Wang, Zhe
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Pfahringer, B

Renz, J

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2015
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Abstract

Answer set programming (ASP) has been extended to possibilistic ASP (PASP), in which the notion of possibilistic stable models is defined for possibilistic logic programs. However, possibilistic inferences that correspond to the three inferences in ordinary possibilistic logic have not been explored in PASP yet. In this paper, based on the skeptical reasoning determined by possibilistic stable models, we define three inference relations for PASP, provide their equivalent characterisations in terms of possibility distribution, and develop algorithms for these possibilistic inferences. Our algorithms are achieved by generalising some important concepts (Clarke’s completion, loop formulas, and guarded resolution) and properties in ASP to PASP. Besides their theoretical importance, these results can be used to develop efficient implementations for possibilistic reasoning in ASP.

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Lecture Notes in Computer Science

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9457

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Other information and computing sciences not elsewhere classified

Information and computing sciences

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