Bridging the Gap between Reinforcement Learning and Knowledge Representation: A Logical Off- and On-Policy Framework

DOI10.1007/978-3-642-22152-1_40zbMath1341.68231arXiv1012.1552OpenAlexW1672997863MaRDI QIDQ3011967

Emad W. Saad

Publication date: 29 June 2011

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

Full work available at URL: https://arxiv.org/abs/1012.1552

Mathematics Subject Classification ID

Analysis of algorithms and problem complexity (68Q25) Learning and adaptive systems in artificial intelligence (68T05) Logic in artificial intelligence (68T27) Knowledge representation (68T30) Computational difficulty of problems (lower bounds, completeness, difficulty of approximation, etc.) (68Q17)

Uses Software

• ASSAT

Cites Work

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• SAT-based planning in complex domains: Concurrency, constraints and nondeterminism
• Contingent planning under uncertainty via stochastic satisfiability
• ASSAT: computing answer sets of a logic program by SAT solvers
• Practical solution techniques for first-order MDPs
• Answer set programming based on propositional satisfiability
• Elevator group control using multiple reinforcement learning agents
• A new approach to hybrid probabilistic logic programs
• On the Relationship between Hybrid Probabilistic Logic Programs and Stochastic Satisfiability
• Probabilistic Reasoning by SAT Solvers
• Domain-dependent knowledge in answer set planning
• Reasoning about actions with sensing under qualitative and probabilistic uncertainty
• Inductive Logic Programming

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