TP-Compilation for inference in probabilistic logic programs

Jonas Vlasselaer, Guy Van den Broeck, Angelika Kimmig, Wannes Meert, Luc De Raedt

Abstract: Highlights • A new inference technique for probabilistic logic programs. • We interleave knowledge compilation with forward reasoning. • Exact as well as anytime approximate inference. • Our approach is conducive to inference in dynamic models. • Empirical evaluation on various domains. Abstract We propose T P -compilation, a new inference technique for probabilistic logic programs that is based on forward reasoning. T P -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 T P -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 T P -compilation is conducive to inference in dynamic domains as it supports efficient updates to the compiled model.