First-order context-specific likelihood weighting in hybrid probabilistic logic programs

Kumar, Nitesh ORCID: https://orcid.org/0000-0002-9301-3876, Kuzelka, Ondrej and De Raedt, Luc 2023. First-order context-specific likelihood weighting in hybrid probabilistic logic programs. Journal of Artificial Intelligence Research 77 , pp. 683-735. 10.1613/jair.1.13657

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Abstract

Statistical relational AI and probabilistic logic programming have so far mostly focused on discrete probabilistic models. The reasons for this is that one needs to provide constructs to succinctly model the independencies in such models, and also provide efficient inference. Three types of independencies are important to represent and exploit for scalable inference in hybrid models: conditional independencies elegantly modeled in Bayesian networks, context-specific independencies naturally represented by logical rules, and independencies amongst attributes of related objects in relational models succinctly expressed by combining rules. This paper introduces a hybrid probabilistic logic programming language, DC#, which integrates distributional clauses' syntax and semantics principles of Bayesian logic programs. It represents the three types of independencies qualitatively. More importantly, we also introduce the scalable inference algorithm FO-CS-LW for DC#. FO-CS-LW is a first-order extension of the context-specific likelihood weighting algorithm (CS-LW), a novel sampling method that exploits conditional independencies and context-specific independencies in ground models. The FO-CS-LW algorithm upgrades CS-LW with unification and combining rules to the first-order case.

Item Type:	Article
Date Type:	Published Online
Status:	Published
Schools:	Computer Science & Informatics
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Publisher:	AI Access Foundation
ISSN:	1076-9757
Funders:	European Research Council (ERC), Czech Science Foundation
Date of First Compliant Deposit:	21 October 2024
Last Modified:	25 Oct 2024 14:30
URI:	https://orca.cardiff.ac.uk/id/eprint/173170

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