Well-definedness and efficient inference for probabilistic logic programming under the distribution semantics

Fabrizio Riguzzi, Terrance Swift

Research output: Contribution to journalArticlepeer-review

34 Scopus citations


Distribution semantics is one of the most prominent approaches for the combination of logic programming and probability theory. Many languages follow this semantics, such as Independent Choice Logic, PRISM, pD, Logic Programs with Annotated Disjunctions (LPADs), and ProbLog. When a program contains functions symbols, the distribution semantics is well-defined only if the set of explanations for a query is finite and so is each explanation. Well-definedness is usually either explicitly imposed or is achieved by severely limiting the class of allowed programs. In this paper, we identify a larger class of programs for which the semantics is well-defined together with an efficient procedure for computing the probability of queries. Since Logic Programs with Annotated Disjunctions offer the most general syntax, we present our results for them, but our results are applicable to all languages under the distribution semantics. We present the algorithm Probabilistic Inference with Tabling and Answer subsumption (PITA) that computes the probability of queries by transforming a probabilistic program into a normal program and then applying SLG resolution with answer subsumption. PITA has been implemented in XSB and tested on six domains: two with function symbols and four without. The execution times are compared with those of ProbLog, cplint, and CVE. PITA was almost always able to solve larger problems in a shorter time, on domains with and without function symbols.

Original languageEnglish (US)
Pages (from-to)279-302
Number of pages24
JournalTheory and Practice of Logic Programming
Issue number2
StatePublished - Mar 2013
Externally publishedYes


  • Answer subsumption
  • Logic programs with annotated disjunction
  • Probabilistic logic programming
  • Program transformation
  • Tabling

ASJC Scopus subject areas

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


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