Tp-compilation for inference inprobabilistic logic programs

• Published in: International journal of approximate reasoning 2016, Vol.78, p.15
• Main Authors: Vlasselaer, Jonas, Van den Broeck, Guy, Kimmig, Angelika, Meert, Wannes, De Raedt, Luc
• Format: Article
• Language: English
• Subjects: Dynamic relational models; Knowledge compilation; Probabilistic inference; Probabilistic logic programs
• ISSN: 1873-4731; 0888-613X
• DOI: 10.1016/j.ijar.2016.06.009

We propose TP" role="presentation">-compilation, a new inference technique for probabilistic logic programs that is based on forward reasoning. TP" role="presentation">-compilation proceeds incrementally in that it interleaves the knowledge compilation step for weighted model counting with forward reasoning on the logic program. This leads to a novel anytime algorithm that provides hard bounds on the inferred probabilities. The main difference with existing inference techniques for probabilistic logic programs is that these are a sequence of isolated transformations. Typically, these transformations include conversion of the ground program into an equivalent propositional formula and compilation of this formula into a more tractable target representation for weighted model counting. An empirical evaluation shows that TP" role="presentation">-compilation effectively handles larger instances of complex or cyclic real-world problems than current sequential approaches, both for exact and anytime approximate inference. Furthermore, we show that TP" role="presentation">-compilation is conducive to inference in dynamic domains as it supports efficient updates to the compiled model.