Markov logic style weighted rules under the stable model semantics

Joohyung Lee, Yunsong Meng, Yi Wang

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

2 Scopus citations

Abstract

We introduce the language LPMLN that extends logic programs under the stable model semantics to allow weighted rules similar to the way Markov Logic considers weighted formulas. LPMLN is a proper extension of the stable model semantics to enable probabilistic reasoning, providing a way to handle inconsistency in answer set programming. We also show that the recently established logical relationship between Pearl's Causal Models and answer set programs can be extended to the probabilistic setting via LPMLN.

Original languageEnglish (US)
Title of host publicationCEUR Workshop Proceedings
PublisherCEUR-WS
Volume1433
StatePublished - 2015
Event31st International Conference on Logic Programming, ICLP 2015 - Cork, Ireland
Duration: Aug 31 2015Sep 4 2015

Other

Other31st International Conference on Logic Programming, ICLP 2015
CountryIreland
CityCork
Period8/31/159/4/15

Keywords

  • Answer set programming
  • Markov logic networks
  • Probabilistic causal models
  • Probabilistic logic programming

ASJC Scopus subject areas

  • Computer Science(all)

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