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European Conference on Symbolic and Quantitative Approaches to Reasoning and Uncertainty

ECSQARU 2017: Symbolic and Quantitative Approaches to Reasoning with Uncertainty pp 449–458Cite as

The Complexity of Inferences and Explanations in Probabilistic Logic Programming

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10369))

Abstract

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.

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Notes

  1. 1.

    IMPORTANT NOTE: Due to space restrictions; we only present proof sketches; the reader can find the complete proofs at http://sites.poli.usp.br/p/fabio.cozman/Publications/Article/cozman-maua-ecsqaru2017.pdf.

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Acknowledgements

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. Escola Politécnica, Universidade de São Paulo, São Paulo, Brazil

    Fabio G. Cozman

  2. Instituto de Matemática e Estatística, Universidade de São Paulo, São Paulo, Brazil

    Denis D. Mauá

Authors
  1. Fabio G. Cozman
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  2. Denis D. Mauá
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Correspondence to Fabio G. Cozman .

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Editors and Affiliations

  1. IDSIA, Lugano, Switzerland

    Alessandro Antonucci

  2. ONERA, Toulouse, France

    Laurence Cholvy

  3. Aix-Marseille University, Marseille, France

    Odile Papini

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Cozman, F.G., Mauá, D.D. (2017). The Complexity of Inferences and Explanations in Probabilistic Logic Programming. In: Antonucci, A., Cholvy, L., Papini, O. (eds) Symbolic and Quantitative Approaches to Reasoning with Uncertainty. ECSQARU 2017. Lecture Notes in Computer Science(), vol 10369. Springer, Cham. https://doi.org/10.1007/978-3-319-61581-3_40

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  • DOI: https://doi.org/10.1007/978-3-319-61581-3_40

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-61580-6

  • Online ISBN: 978-3-319-61581-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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