The Complexity of Inferences and Explanations in Probabilistic Logic Programming

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10369)


A popular family of probabilistic logic programming languages combines logic programs with independent probabilistic facts. We study the complexity of marginal inference, most probable explanations, and maximum a posteriori calculations for propositional/relational probabilistic logic programs that are acyclic/definite/stratified/normal/ disjunctive. We show that complexity classes \(\varSigma _k\) and \(\mathsf {PP}^{\varSigma _k}\) (for various values of k) and \(\mathsf {NP}^\mathsf {PP}\) are all reached by such computations.



The first author is partially supported by CNPq, grant 308433/2014-9. The second author received support from the São Paulo Research Foundation (FAPESP), grant 2016/01055-1. The work reported in this paper was partially funded by FAPESP grant #2015/21880-4 (project Proverbs).


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Authors and Affiliations

  1. 1.Escola PolitécnicaUniversidade de São PauloSão PauloBrazil
  2. 2.Instituto de Matemática e EstatísticaUniversidade de São PauloSão PauloBrazil

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