Probabilistic reasoning with answer sets

Chitta Baral, Michael Gelfond, Nelson Rushton

Research output: Contribution to journalArticlepeer-review

203 Scopus citations


This paper develops a declarative language, P-log, that combines logical and probabilistic arguments in its reasoning. Answer Set Prolog is used as the logical foundation, while causal Bayes nets serve as a probabilistic foundation. We give several non-trivial examples and illustrate the use of P-log for knowledge representation and updating of knowledge. We argue that our approach to updates is more appealing than existing approaches. We give sufficiency conditions for the coherency of P-log programs and show that Bayes nets can be easily mapped to coherent P-log programs.

Original languageEnglish (US)
Pages (from-to)57-144
Number of pages88
JournalTheory and Practice of Logic Programming
Issue number1
StatePublished - Jan 2009


  • Answer Set Prolog
  • Answer sets
  • Logic programming
  • Probabilistic reasoning

ASJC Scopus subject areas

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


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