The Complexity of Inferences and Explanations in Probabilistic Logic Programming

@inproceedings{Cozman2017TheCO,
  title={The Complexity of Inferences and Explanations in Probabilistic Logic Programming},
  author={Fabio Gagliardi Cozman and D. Mau{\'a}},
  booktitle={ECSQARU},
  year={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 \(\varSigma _k\) and \(\mathsf {PP}^{\varSigma _k}\) (for various values of k) and \(\mathsf {NP}^\mathsf {PP… 

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