Explanation-based approximate weighted model counting for probabilistic logics

Renkens, Joris, Kimmig, Angelika ORCID: https://orcid.org/0000-0002-6742-4057, Broeck, Guy Van den and Raedt, Luc De 2014. Explanation-based approximate weighted model counting for probabilistic logics. Presented at: 28th AAAI Conference on Artificial Intelligence, Quebec, Canada, 27-31 July 2014. AAAIWS'14-13 Proceedings of the 13th AAAI Conference on Statistical Relational AI. AAAI Press,

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Abstract

Probabilistic inference can be realized using weighted model counting. Despite a lot of progress, computing weighted model counts exactly is still infeasible for many problems of interest, and one typically has to resort to approximation methods. We contribute a new bounded approximation method for weighted model counting based on probabilistic logic programming principles. Our bounded approximation algorithm is an anytime algorithm that provides lower and upper bounds on the weighted model count. An empirical evaluation on probabilistic logic programs shows that our approach is effective in many cases that are currently beyond the reach of exact methods.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics
Publisher:	AAAI Press
Date of First Compliant Deposit:	5 August 2019
Date of Acceptance:	8 April 2014
Last Modified:	26 Oct 2022 07:23
URI:	https://orca.cardiff.ac.uk/id/eprint/124715

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