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Anytime Inference in Probabilistic Logic Programs with TP-Compilation
Department of Computer Science, KU Leuven, Leuven, Belgium.
Department of Computer Science, KU Leuven, Leuven, Belgium.
Department of Computer Science, KU Leuven, Leuven, Belgium.
Department of Computer Science, KU Leuven, Leuven, Belgium.
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2015 (English)In: Proceedings of 24th International Joint Conference on ArtificialIntelligence (IJCAI) / [ed] Qiang Yang; Michael Wooldridge, AAAI Press, 2015, p. 1852-1858Conference paper, Published paper (Refereed)
Abstract [en]

Existing techniques for inference in probabilisticlogic programs are sequential: they first computethe relevant propositional formula for the query ofinterest, then compile it into a tractable target rep-resentation and finally, perform weighted modelcounting on the resulting representation. We pro-poseTP-compilation, a new inference techniquebased on forward reasoning.TP-compilation pro-ceeds incrementally in that it interleaves the knowl-edge compilation step for weighted model countingwith forward reasoning on the logic program. Thisleads to a novel anytime algorithm that provideshard bounds on the inferred probabilities. Fur-thermore, an empirical evaluation shows thatTP-compilation effectively handles larger instances ofcomplex real-world problems than current sequen-tial approaches, both for exact and for anytime ap-proximate inference.

Place, publisher, year, edition, pages
AAAI Press, 2015. p. 1852-1858
Series
IJCAI International Joint Conference on Artificial Intelligence, ISSN 1045-0823
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:oru:diva-91829ISBN: 9781577357384 (print)OAI: oai:DiVA.org:oru-91829DiVA, id: diva2:1554815
Conference
24th International Joint Conference on Artificial Intelligence (IJCAI 2015), Buenos Aires, Argentina, July 25-31, 2015
Available from: 2021-05-17 Created: 2021-05-17 Last updated: 2021-05-18Bibliographically approved

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De Raedt, Luc

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