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Search methods based on Monte-Carlo simulation

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Search methods based on Monte-Carlo simulation have revolutionized difficult game-playing domains such as Go and General Game Playing. Monte-Carlo Random Walk planning applies Monte-Carlo ideas to deterministic classical planning. The forward chaining planner Arvand uses such Monte-Carlo random walks to explore the local neighborhood of a search state for action selection. Random walks yield a large unbiased sample of the search neighborhood, and require expensive state evaluations only at the endpoints of each walk. The result is a relatively simple but powerful planner that is competitive with state of the art systems. The talk introduces the main ideas of planning using Monte-Carlo random walks, and briefly discusses recent work to improve plan quality and to improve planning with limited resources.

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