The principal continuation and the killer heuristic
ACM '77 Proceedings of the 1977 annual conference
Combining online and offline knowledge in UCT
Proceedings of the 24th international conference on Machine learning
Proceedings of the 25th international conference on Machine learning
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
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
Creating an upper-confidence-tree program for havannah
ACG'09 Proceedings of the 12th international conference on Advances in Computer Games
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Havannah is a game played on an hexagonal board of hexagons where the base of the board typically ranges from four to ten hexagons. The game is known to be difficult to program. We study an MCTS-based approach to programming Havannah using our program named WANDERER. We experiment with five techniques of the basic MCTS algorithms and demonstrate that at normal time controls of approximately 30 seconds per move WANDERER can make quite strong moves with bases of size four or five, and play a reasonable game with bases of size six or seven. At longer time controls (ten minutes per move) WANDERER (1) appears to play nearly perfectly with base four, (2) is difficult for most humans to beat at base five, and (3) gives a good game at bases six and seven. Future research focuses on larger board sizes.