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 Σk and PPΣk (for various values of k) and NPPP are all reached by such computations.
Cite
CITATION STYLE
Cozman, F. G., & Mauá, D. D. (2017). The complexity of inferences and explanations in probabilistic logic programming. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10369 LNAI, pp. 449–458). Springer Verlag. https://doi.org/10.1007/978-3-319-61581-3_40
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