Inference in Probabilistic Logic Programs using Weighted CNF's
This work addresses inference challenges in probabilistic logic programming, an incremental advance for researchers in that domain.
The paper tackled the problem of performing classical probabilistic inference tasks like MAP and marginals in probabilistic logic programs, which had not received much attention, by developing efficient algorithms that convert programs to weighted CNF formulas and use weighted model counting, resulting in experimental improvements over the state-of-the-art.
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.