The Nonstochastic Multiarmed Bandit Problem
SIAM Journal on Computing
Prediction, Learning, and Games
Prediction, Learning, and Games
Game-tree search with combinatorially large belief states
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Monte Carlo tree search techniques in the game of Kriegspiel
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Efficient selectivity and backup operators in Monte-Carlo tree search
CG'06 Proceedings of the 5th international conference on Computers and games
Bandit based monte-carlo planning
ECML'06 Proceedings of the 17th European conference on Machine Learning
ACG'05 Proceedings of the 11th international conference on Advances in Computer Games
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We propose an algorithm for computing approximate Nash equilibria of partially observable games using Monte-Carlo tree search based on recent bandit methods. We obtain experimental results for the game of phantom tic-tac-toe, showing that strong strategies can be efficiently computed by our algorithm.