The distribution semantics for normal programs with function symbols

DOI10.1016/j.ijar.2016.05.005zbMath1385.68022OpenAlexW2418060149WikidataQ58063597 ScholiaQ58063597MaRDI QIDQ313122

Fabrizio Riguzzi

Publication date: 9 September 2016

Published in: International Journal of Approximate Reasoning (Search for Journal in Brave)

Full work available at URL: http://hdl.handle.net/11392/2350945

zbMATH Keywords

distribution semanticsfunction symbolsprobabilistic logic programmingProbLog

Mathematics Subject Classification ID

Semantics in the theory of computing (68Q55) Logic programming (68N17)

Related Items (7)

A survey of lifted inference approaches for probabilistic logic programming under the distribution semantics ⋮ \(T_{\mathcal{P}}\)-compilation for inference in probabilistic logic programs ⋮ Causal inference in cplint ⋮ Lifted discriminative learning of probabilistic logic programs ⋮ MAP Inference for Probabilistic Logic Programming ⋮ A semantics for hybrid probabilistic logic programs with function symbols ⋮ Abduction with probabilistic logic programming under the distribution semantics

Uses Software

• Oz
• ProbLog
• CP-logic
• PITA
• BLOG
• MCINTYRE

Cites Work

• \(T_{\mathcal{P}}\)-compilation for inference in probabilistic logic programs
• Logic programming, abduction and probability. A top-down anytime algorithm for estimating prior and posterior probabilities
• Probabilistic (logic) programming concepts
• Probabilistic Horn abduction and Bayesian networks
• The independent choice logic for modelling multiple agents under uncertainty
• Infinite probability computation by cyclic explanation graphs
• Terminating Evaluation of Logic Programs with Finite Three-Valued Models
• On the implementation of the probabilistic logic programming language ProbLog
• The PITA system: Tabling and answer subsumption for reasoning under uncertainty
• CP-logic: A language of causal probabilistic events and its relation to logic programming
• The well-founded semantics for general logic programs
• Abducing through negation as failure: stable models within the independent choice logic
• Model checking with probabilistic tabled logic programming
• Well–definedness and efficient inference for probabilistic logic programming under the distribution semantics
• Logic Programming
• Tabling for infinite probability computation
• The Independent Choice Logic and Beyond
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