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Learning possibilistic logic theories from default rules (abridged version)

Kuzelka, Ondrej, Davis, Jesse and Schockaert, Steven ORCID: 2017. Learning possibilistic logic theories from default rules (abridged version). Presented at: IJCAI-17: Workshop on Logical Foundations for Uncertainty and Machine Learning, Melbourne, Australia, 19-25 August 2017.

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We introduce a setting for learning possibilistic logic theories from defaults of the form “if alpha then typically beta”. An important property of our approach is that it is inherently able to handle noisy and conflicting sets of defaults. Among others, this allows us to learn possibilistic logic theories from crowdsourced data and to approximate propositional Markov logic networks using heuristic MAP solvers.

Item Type: Conference or Workshop Item (Paper)
Date Type: Completion
Status: Unpublished
Schools: Computer Science & Informatics
Date of First Compliant Deposit: 22 August 2017
Last Modified: 22 Oct 2022 13:30

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