The complexity of inferences and explanations in probabilistic logic programming

Fabio G. Cozman, Denis D. Mauá

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

© Springer International Publishing AG 2017. 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 Σk and PPΣk (for various values of k) and NPPP are all reached by such computations.
Original languageAmerican English
Title of host publicationThe complexity of inferences and explanations in probabilistic logic programming
Pages449-458
Number of pages10
ISBN (Electronic)9783319615806
DOIs
StatePublished - 1 Jan 2017
Externally publishedYes
EventLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) -
Duration: 1 Jan 2018 → …

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10369 LNAI
ISSN (Print)0302-9743

Conference

ConferenceLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Period1/01/18 → …

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