Discovering hidden structure in factored MDPs

DOI10.1016/j.artint.2012.05.002zbMath1251.68206OpenAlexW2123557770MaRDI QIDQ1761300

Mausam, Daniel S. Weld, Andrey V. Kolobov

Publication date: 15 November 2012

Published in: Artificial Intelligence (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.artint.2012.05.002

zbMATH Keywords

Markov decision processheuristicgeneralizationabstractionbasis functionMDPplanning under uncertaintydead endnogood

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Approximation methods and heuristics in mathematical programming (90C59) Markov and semi-Markov decision processes (90C40) Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.) (68T20)

Related Items (4)

Real-time dynamic programming for Markov decision processes with imprecise probabilities ⋮ State space search nogood learning: online refinement of critical-path dead-end detectors in planning ⋮ Discovering hidden structure in factored MDPs ⋮ A Markov decision model with dead ends for operating room planning considering dynamic patient priority

Uses Software

• Graphplan

Cites Work

• The factored policy-gradient planner
• Practical solution techniques for first-order MDPs
• The computational complexity of propositional STRIPS planning
• Failure driven dynamic search control for partial order planners: an explanation based approach
• Fast planning through planning graph analysis
• Discovering hidden structure in factored MDPs
• LAO*: A heuristic search algorithm that finds solutions with loops
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