Compacting boolean formulae for inference in probabilistic logic programming

Knowledge compilation converts Boolean formulae for which some inference tasks are computationally expensive into a representation where the same tasks are tractable. ProbLog is a state-of-the-art Probabilistic Logic Programming system that uses knowledge compilation to reduce the expensive probabilistic inference to an efficient weighted model counting. Motivated to improve ProbLog’s performance we present an approach that optimizes Boolean formulae in order to speed-up knowledge compilation. We identify 7 Boolean subformulae patterns that can be used to re-write Boolean formulae.We implemented an algorithm with polynomial complexity which detects and compacts 6 of these patterns. We employ our method in the inference pipeline of ProbLog and conduct extensive experiments. We show that our compaction method improves knowledge compilation and consecutively the overall inference performance. Furthermore, using compaction reduces the number of time-outs, allowing us to solve previously unsolvable problems.