Probabilistic Rule Learning in Nonmonotonic Domains

DOI10.1007/978-3-642-22359-4_17zbMath1348.68188OpenAlexW2134073653MaRDI QIDQ3092307

Katsumi Inoue, Alessandra Russo, Daniel Sykes, Domenico Corapi

Publication date: 16 September 2011

Published in: Lecture Notes in Computer Science (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/978-3-642-22359-4_17

zbMATH Keywords

planninginductive logic programminganswer set programmingprobabilistic logic programminghypothetical reasoning

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Logic programming (68N17) Probability in computer science (algorithm analysis, random structures, phase transitions, etc.) (68Q87)

Related Items (4)

Rule Induction and Reasoning over Knowledge Graphs ⋮ Making sense of sensory input ⋮ Learning from interpretation transition ⋮ A Tool for Probabilistic Reasoning Based on Logic Programming and First-Order Theories Under Stable Model Semantics

Uses Software

• ProbLog

Cites Work

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• A logic-based calculus of events
• Nonmonotonic abductive inductive learning
• Answer set programming and plan generation
• Markov logic networks
• INDUCTIVE LOGIC PROGRAMMING AS ABDUCTIVE SEARCH
• Probabilistic Rule Learning
• Abducing through negation as failure: stable models within the independent choice logic
• An abductive event calculus planner
• Extracting Requirements from Scenarios with ILP
• On the Efficient Execution of ProbLog Programs

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