Weighted Rules under the Stable Model Semantics

Abstract

We introduce the concept of weighted rules under the stable model semantics following the log-linear models of Markov Logic. This provides versatile methods to overcome the deterministic nature of the stable model semantics, such as resolving inconsistencies in answer set programs, ranking stable models, associating probability to stable models, and applying statistical inference to computing weighted stable models. We also present formal comparisons with related formalisms, such as answer set programs, Markov Logic, ProbLog, and P-log.

References in corpus (2)

• Possibilistic Answer Set Programming Revisited
• Probabilistic Inductive Logic Programming Based on Answer Set Programming

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