\(T_{\mathcal{P}}\)-compilation for inference in probabilistic logic programs

DOI10.1016/j.ijar.2016.06.009zbMath1386.68174OpenAlexW2465254145MaRDI QIDQ324656

Angelika Kimmig, Wannes Meert, Jonas Vlasselaer, Guy Van den Broeck, Luc De Raedt

Publication date: 17 October 2016

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

Full work available at URL: https://doi.org/10.1016/j.ijar.2016.06.009

zbMATH Keywords

probabilistic inferencedynamic relational modelsknowledge compilationprobabilistic logic programs

Mathematics Subject Classification ID

Reasoning under uncertainty in the context of artificial intelligence (68T37) Logic programming (68N17)

Related Items (6)

The distribution semantics for normal programs with function symbols ⋮ \(T_{\mathcal{P}}\)-compilation for inference in probabilistic logic programs ⋮ kProbLog: an algebraic Prolog for machine learning ⋮ kProbLog: An Algebraic Prolog for Kernel Programming ⋮ Challenges of real-world reinforcement learning: definitions, benchmarks and analysis ⋮ Lifted Dynamic Junction Tree Algorithm

Uses Software

• ProbLog
• CP-logic
• PITA
• Church
• BLOG
• SPINE

Cites Work

• The distribution semantics for normal programs with function symbols
• \(T_{\mathcal{P}}\)-compilation for inference in probabilistic logic programs
• Exploiting symmetries for scaling loopy belief propagation and relational training
• Stochastic relational processes: efficient inference and applications
• Logic programming, abduction and probability. A top-down anytime algorithm for estimating prior and posterior probabilities
• Probabilistic (logic) programming concepts
• Planning in hybrid relational MDPs
• \(k\)-optimal: a novel approximate inference algorithm for ProbLog
• On probabilistic inference by weighted model counting
• Probabilistic inductive logic programming. Theory and applications
• Compiling relational Bayesian networks for exact inference
• Probabilistic logic programming for hybrid relational domains
• Exploiting local and repeated structure in dynamic Bayesian networks
• The PITA system: Tabling and answer subsumption for reasoning under uncertainty
• The magic of logical inference in probabilistic programming
• Probabilistic Databases
• CP-logic: A language of causal probabilistic events and its relation to logic programming
• $$\#\exists $$ SAT: Projected Model Counting
• Modeling and Reasoning with Bayesian Networks
• Inference and learning in probabilistic logic programs using weighted Boolean formulas
• Knowledge compilation of logic programs using approximation fixpoint theory
• Logic Programming
• Compiling Probabilistic Graphical Models Using Sentential Decision Diagrams
• Revising Probabilistic Prolog Programs
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