Symbolic Heuristic Search for Probabilistic Planning
نویسندگان
چکیده
We describe a planner that participates in the Probabilistic Planning Track of the 2004 International Planning Competition. Our planner integrates two approaches to solving Markov decision processes with large state spaces. State abstraction is used to avoid evaluating states individually. Forward search from a start state, guided by an admissible heuristic, is used to avoid evaluating all states.
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