Search methods based on Monte-Carlo simulation have recently led to breakthrough performance improvements in difficult game-playing domains such as Go and General Game Playing. Monte-Carlo Random Walk (MRW) planning applies Monte- Carlo ideas to deterministic classical planning. In the forward chaining planner ARVAND, Monte- Carlo random walks are used to explore the local neighborhood of a search state for action selection. In contrast to the stochastic local search approach used in the recent planner Identidem, random walks yield a larger and unbiased sample of the search neighborhood, and require state evaluations only at the endpoints of each walk. On IPC-4 competition problems, the performance of ARVAND is competitive with state of the art systems.

Authors

• Hootan Nakhost University of Alberta
• Martin Müller

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