Existing techniques for inference in probabilistic logic programs are sequential: they first compute the relevant propositional formula for the query of interest, then compile it into a tractable target representation and finally, perform weighted model counting on the resulting representation. We propose TP-compilation, a new inference technique based on forward reasoning. TP-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. Furthermore, an empirical evaluation shows that TP-compilation effectively handles larger instances of complex real-world problems than current sequential approaches, both for exact and for anytime approximate inference.

@inproceedings{VlasselaerIJCAI15,
  author    = {Vlasselaer, Jonas and Van den Broeck, Guy and Kimmig, Angelika and Meert, Wannes and De Raedt, Luc},
  title     = {Anytime Inference in Probabilistic Logic Programs with {Tp}-compilation},
  booktitle = {Proceedings of 24th International Joint Conference on Artificial Intelligence (IJCAI)},
  year      = {2015},
  url = {http://starai.cs.ucla.edu/papers/VlasselaerIJCAI15.pdf},
  keywords   = {conference,selective}
}