CLP( $\cal{BN}$ ): Constraint Logic Programming for Probabilistic Knowledge

DOI10.1007/978-3-540-78652-8_6zbMath1137.68342OpenAlexW2119493804MaRDI QIDQ5452025

David Page, Vitor Costa, James Cussens

Publication date: 28 March 2008

Published in: Probabilistic Inductive Logic Programming (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/978-3-540-78652-8_6

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Logic programming (68N17)

Related Items (8)

The YAP Prolog system ⋮ Probabilistic (logic) programming concepts ⋮ A landscape and implementation framework for probabilistic rough sets using \textsc{ProbLog} ⋮ Quantitative pharmacophore models with inductive logic programming ⋮ Imprecise probabilistic query answering using measures of ignorance and degree of satisfaction ⋮ A Survey of Directed Entity-Relation--Based First-Order Probabilistic Languages ⋮ Unnamed Item ⋮ ALLPAD: approximate learning of logic programs with annotated disjunctions

Uses Software

• ProbLog
• YAP-Prolog
• BNT
• BUGS

Cites Work

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• Probabilistic Horn abduction and Bayesian networks
• Representations and solutions for game-theoretic problems
• Bucket elimination: A unifying framework for reasoning
• Markov logic networks
• On applying or-parallelism and tabling to logic programs
• Inferring Regulatory Networks from Time Series Expression Data and Relational Data Via Inductive Logic Programming
• Algorithmic Learning Theory

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