Combining online and offline knowledge in UCT
Proceedings of the 24th international conference on Machine learning
Single-Player Monte-Carlo Tree Search
CG '08 Proceedings of the 6th international conference on Computers and Games
CG '08 Proceedings of the 6th international conference on Computers and Games
Monte-Carlo Tree Search Solver
CG '08 Proceedings of the 6th international conference on Computers and Games
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
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
Evaluation function based monte-carlo LOA
ACG'09 Proceedings of the 12th international conference on Advances in Computer Games
Recognizing seki in computer go
ACG'05 Proceedings of the 11th international conference on Advances in Computer Games
ACG'05 Proceedings of the 11th international conference on Advances in Computer Games
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Monte-Carlo Tree Search (MCTS) is a successful algorithm used in many state of the art game engines. We propose to improve a MCTS solver when a game has more than two outcomes. It is for example the case in games that can end in draw positions. In this case it improves significantly a MCTS solver to take into account bounds on the possible scores of a node in order to select the nodes to explore. We apply our algorithm to solving Seki in the game of Go and to Connect Four.