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ICTAI
2005
IEEE
14 years 1 months ago
Planning with POMDPs Using a Compact, Logic-Based Representation
Partially Observable Markov Decision Processes (POMDPs) provide a general framework for AI planning, but they lack the structure for representing real world planning problems in a...
Chenggang Wang, James G. Schmolze
AIPS
2000
13 years 8 months ago
New Results about LCGP, a Least Committed GraphPlan
Planners from the family of Graphplan (Graphplan, IPP, STAN...) are presently considered as the most efficient ones on numerous planning domains. Their partially ordered plans can...
Michel Cayrol, Pierre Régnier, Vincent Vida...
AI
2008
Springer
13 years 7 months ago
Sequential Monte Carlo in reachability heuristics for probabilistic planning
The current best conformant probabilistic planners encode the problem as a bounded length CSP or SAT problem. While these approaches can find optimal solutions for given plan leng...
Daniel Bryce, Subbarao Kambhampati, David E. Smith
AIPS
2006
13 years 8 months ago
Sequential Monte Carlo in Probabilistic Planning Reachability Heuristics
The current best conformant probabilistic planners encode the problem as a bounded length CSP or SAT problem. While these approaches can find optimal solutions for given plan leng...
Daniel Bryce, Subbarao Kambhampati, David E. Smith