Weighted rules under the stable model semantics

Joohyung Lee, Yi Wang

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

21 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationPrinciples of Knowledge Representation and Reasoning: Proceedings of the 15th International Conference, KR 2016
PublisherAAAI press
Pages145-154
Number of pages10
StatePublished - 2016
Event15th International Conference on Principles of Knowledge Representation and Reasoning, KR 2016 - Cape Town, South Africa
Duration: Apr 25 2016Apr 29 2016

Other

Other15th International Conference on Principles of Knowledge Representation and Reasoning, KR 2016
CountrySouth Africa
CityCape Town
Period4/25/164/29/16

ASJC Scopus subject areas

  • Logic
  • Software

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    Lee, J., & Wang, Y. (2016). Weighted rules under the stable model semantics. In Principles of Knowledge Representation and Reasoning: Proceedings of the 15th International Conference, KR 2016 (pp. 145-154). AAAI press.