Quantified Markov logic networks

Gutierrez Basulto, Victor ORCID: https://orcid.org/0000-0002-6117-5459, Jung, Jean Christoph and Kuzelka, Ondrej 2018. Quantified Markov logic networks. Presented at: 16th International Conference on Principles of Knowledge Representation and Reasoning (KR-18), Tempe, Arizona, USA, 30 October-2 November 2018. International Conference on Principles of Knowledge Representation and Reasoning.

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Abstract

Markov Logic Networks (MLNs) are well-suited for expressing statistics such as “with high probability a smoker knows another smoker” but not for expressing statements such as “there is a smoker who knows most other smokers”, which is necessary for modeling, e.g. influencers in social networks. To overcome this shortcoming, we study quantified MLNs which generalize MLNs by introducing statistical universal quantifiers, allowing to express also the latter type of statistics in a principled way. Our main technical contribution is to show that the standard reasoning tasks in quantified MLNs, maximum a posteriori and marginal inference, can be reduced to their respective MLN counterparts in polynomial time.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics
Funders:	Horizon 2020 programme under the Marie Sk?odowska-Curie grant 663830
Related URLs:	Publisher
Date of First Compliant Deposit:	16 August 2018
Date of Acceptance:	11 July 2018
Last Modified:	24 Oct 2022 07:10
URI:	https://orca.cardiff.ac.uk/id/eprint/114245

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