Encoding Markov logic networks in possibilistic logic

Kuzelka, Ondrej, Davis, Jesse and Schockaert, Steven ORCID: https://orcid.org/0000-0002-9256-2881 2015. Encoding Markov logic networks in possibilistic logic. Presented at: 31st Conference on Uncertainty in Artificial Intelligence, Amsterdam, 12-16 July 2015. Published in: Meila, M. and Heskes, T. eds. Uncertainty in Artificial Intelligence - Proceedings of the 31st Conference, UAI 2015. AUAI Press, pp. 454-463.

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Abstract

Markov logic uses weighted formulas to compactly encode a probability distribution over possible worlds. Despite the use of logical formulas, Markov logic networks (MLNs) can be difficult to interpret, due to the often counter-intuitive meaning of their weights. To address this issue, we propose a method to construct a possibilistic logic theory that exactly captures what can be derived from a given MLN using maximum a posteriori (MAP) inference. Unfortunately, the size of this theory is exponential in general. We therefore also propose two methods which can derive compact theories that still capture MAP inference, but only for specific types of evidence. These theories can be used, among others, to make explicit the hidden assumptions underlying an MLN or to explain the predictions it makes.

Item Type:	Conference or Workshop Item - published (Paper)
Date Type:	Publication
Status:	Published
Schools:	Schools > Computer Science & Informatics
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Publisher:	AUAI Press
ISBN:	978-099664310-8
Funders:	Leverhulme trust
Last Modified:	12 Oct 2026 13:55
URI:	https://orca.cardiff.ac.uk/id/eprint/76129

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