Proceedings

Anytime Inference in Probabilistic Logic Programs with Tp-Compilation

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

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Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence. Main Track. Pages 1852-1857.

Abstract

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.

Jonas Vlasselaer, Guy Van den Broeck, Angelika Kimmig, Wannes Meert, and Luc de Raedt. 2015. Anytime Inference in Probabilistic Logic Programs with Tp-Compilation. In Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-15. IJCAI, 1852–1857. https://ijcai.org/proceedings/2015/263
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