Vlasselaer, Jonas, Van den Broeck, Guy, Kimmig, Angelika ![]() ![]() |
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Abstract
We propose TP -compilation, a new inference technique for probabilistic logic programs that is 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. 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 -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 -compilation is conducive to inference in dynamic domains as it supports efficient updates to the compiled model.
Item Type: | Article |
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Date Type: | Publication |
Status: | Published |
Schools: | Computer Science & Informatics |
Publisher: | Elsevier |
ISSN: | 0888-613X |
Date of First Compliant Deposit: | 20 November 2017 |
Date of Acceptance: | 9 June 2016 |
Last Modified: | 27 Nov 2024 10:30 |
URI: | https://orca.cardiff.ac.uk/id/eprint/106734 |
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