Computing LP using ASP and MLN solvers

Joohyung Lee, Samidh Talsania, Yi Wang

Research output: Contribution to journalArticlepeer-review

19 Scopus citations

Abstract

LPMLN is a recent addition to probabilistic logic programming languages. Its main idea is to overcome the rigid nature of the stable model semantics by assigning a weight to each rule in a way similar to Markov Logic is defined. We present two implementations of LPMLN, lpmln2asp and lpmln2mln. System lpmln2asp translates LPMLN programs into the input language of answer set solver clingo, and using weak constraints and stable model enumeration, it can compute most probable stable models as well as exact conditional and marginal probabilities. System lpmln2mln translates LPMLN programs into the input language of Markov Logic solvers, such as alchemy, tuffy, and rockit, and allows for performing approximate probabilistic inference on LPMLN programs. We also demonstrate the usefulness of the LPMLN systems for computing other languages, such as ProbLog and Pearl's Causal Models, that are shown to be translatable into LPMLN.

Original languageEnglish (US)
Pages (from-to)942-960
Number of pages19
JournalTheory and Practice of Logic Programming
Volume17
Issue number5-6
DOIs
StatePublished - Sep 1 2017

Keywords

  • Answer Set Programming
  • LPMLN
  • Markov Logic

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Hardware and Architecture
  • Computational Theory and Mathematics
  • Artificial Intelligence

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