A probabilistic extension of the stable model semantics

Joohyung Lee, Yi Wang

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

22 Scopus citations

Abstract

We present a probabilistic extension of logic programs under the stable model semantics, inspired by the idea of Markov Logic Networks. The proposed language, called LPMLN, is a generalization of logic programs under the stable model semantics, and as such, embraces the rich body of research in knowledge representation. The language is also a generalization of ProbLog, and is closely related to Markov Logic Networks, which implies that the computation can be carried out by the techniques developed for them. LPMLN appears to be a natural language for probabilistic answer set programming, and as an example we show how an elaboration tolerant representation of transition systems in answer set programs can be naturally extended to the probabilistic setting.

Original languageEnglish (US)
Title of host publicationLogical Formalizations of Commonsense Reasoning - Papers from the AAAI Spring Symposium, Technical Report
PublisherAI Access Foundation
Pages96-102
Number of pages7
VolumeSS-15-04
ISBN (Electronic)9781577357087
StatePublished - 2015
Event2015 AAAI Spring Symposium - Palo Alto, United States
Duration: Mar 23 2015Mar 25 2015

Other

Other2015 AAAI Spring Symposium
CountryUnited States
CityPalo Alto
Period3/23/153/25/15

ASJC Scopus subject areas

  • Artificial Intelligence

Cite this

Lee, J., & Wang, Y. (2015). A probabilistic extension of the stable model semantics. In Logical Formalizations of Commonsense Reasoning - Papers from the AAAI Spring Symposium, Technical Report (Vol. SS-15-04, pp. 96-102). AI Access Foundation.