Expected-Outcome: A General Model of Static Evaluation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Artificial Intelligence
Combining online and offline knowledge in UCT
Proceedings of the 24th international conference on Machine learning
Information Sciences: an International Journal
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
Score bounded Monte-Carlo tree search
CG'10 Proceedings of the 7th international conference on Computers and games
Node-expansion operators for the UCT algorithm
CG'10 Proceedings of the 7th international conference on Computers and games
Enhancements for multi-player Monte-Carlo tree search
CG'10 Proceedings of the 7th international conference on Computers and games
Evaluation function based monte-carlo LOA
ACG'09 Proceedings of the 12th international conference on Advances in Computer Games
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Recently, Monte-Carlo Tree Search (MCTS) has advanced the field of computer Go substantially. In this article we investigate the application of MCTS for the game Lines of Action (LOA). A new MCTS variant, called MCTS-Solver, has been designed to play narrow tactical lines better in sudden-death games such as LOA. The variant differs from the traditional MCTS in respect to backpropagation and selection strategy. It is able to prove the game-theoretical value of a position given sufficient time. Experiments show that a Monte-Carlo LOA program using MCTS-Solver defeats a program using MCTS by a winning score of 65%. Moreover, MCTS-Solver performs much better than a program using MCTS against several different versions of the world-class 驴βprogram MIA. Thus, MCTS-Solver constitutes genuine progress in using simulation-based search approaches in sudden-death games, significantly improving upon MCTS-based programs.