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Knowledge Compilation and Weighted Model Counting for Inference in Probabilistic Logic Programs
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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2016 (English)In: Proceedings of the First Workshop on Beyond NP, Association for the Advancement of Artificial Intelligence , 2016, p. 359-364Conference paper, Oral presentation with published abstract (Refereed)
Abstract [en]

Over the last decade, building on advances in the areas of knowledge compilation and weighted model counting has drastically increased the scalability of inference in probabilistic logic programs. In this paper, we provide an overview of how this has been possible and point out some open challenges.

Place, publisher, year, edition, pages
Association for the Advancement of Artificial Intelligence , 2016. p. 359-364
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:oru:diva-91388OAI: oai:DiVA.org:oru-91388DiVA, id: diva2:1546530
Conference
The AAAI-16 Workshop on Beyond NP, Phoenix, Arizona, USA, February 12-13, 2016
Available from: 2021-04-22 Created: 2021-04-22 Last updated: 2021-04-22Bibliographically approved

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De Raedt, Luc

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