Inference in Probabilistic Logic Programs using Weighted CNF's

TL;DR

This paper introduces efficient algorithms for probabilistic logic programming inference by converting programs into weighted CNF formulas, enabling the use of advanced weighted model counting techniques.

Contribution

It presents a novel conversion approach from probabilistic logic programs to weighted CNF, facilitating improved inference algorithms.

Findings

Outperforms existing probabilistic logic programming methods

Effective conversion techniques for probabilistic logic programs to weighted CNF

Experimental results show significant efficiency improvements

Abstract

Probabilistic logic programs are logic programs in which some of the facts are annotated with probabilities. Several classical probabilistic inference tasks (such as MAP and computing marginals) have not yet received a lot of attention for this formalism. The contribution of this paper is that we develop efficient inference algorithms for these tasks. This is based on a conversion of the probabilistic logic program and the query and evidence to a weighted CNF formula. This allows us to reduce the inference tasks to well-studied tasks such as weighted model counting. To solve such tasks, we employ state-of-the-art methods. We consider multiple methods for the conversion of the programs as well as for inference on the weighted CNF. The resulting approach is evaluated experimentally and shown to improve upon the state-of-the-art in probabilistic logic programming.