The History Heuristic and Alpha-Beta Search Enhancements in Practice
IEEE Transactions on Pattern Analysis and Machine Intelligence
Gambling in a rigged casino: The adversarial multi-armed bandit problem
FOCS '95 Proceedings of the 36th Annual Symposium on Foundations of Computer Science
Stochastic Local Search: Foundations & Applications
Stochastic Local Search: Foundations & Applications
Planning Algorithms
Simulation-based approach to general game playing
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 1
Achieving master level play in 9×9 computer go
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
The FF planning system: fast plan generation through heuristic search
Journal of Artificial Intelligence Research
The fast downward planning system
Journal of Artificial Intelligence Research
Temporal planning using subgoal partitioning and resolution in SGPlan
Journal of Artificial Intelligence Research
Marvin: a heuristic search planner with online macro-action learning
Journal of Artificial Intelligence Research
Bandit based monte-carlo planning
ECML'06 Proceedings of the 17th European conference on Machine Learning
Revisiting Monte-Carlo tree search on a normal form game: NoGo
EvoApplications'11 Proceedings of the 2011 international conference on Applications of evolutionary computation - Volume Part I
The grand challenge of computer Go: Monte Carlo tree search and extensions
Communications of the ACM
Continuous upper confidence trees
LION'05 Proceedings of the 5th international conference on Learning and Intelligent Optimization
Nested rollout policy adaptation for Monte Carlo tree search
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume One
Towards a second generation random walk planner: an experimental exploration
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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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.