The Complexity of Inferences and Explanations in Probabilistic Logic Programming
نویسندگان
چکیده
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 PP Σk (for various values of k) and NP are all reached by such computations.
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تاریخ انتشار 2017