Monte Carlo Tree Search in Lines of Action

  title={Monte Carlo Tree Search in Lines of Action},
  author={Mark H. M. Winands and Yngvi Bj{\"o}rnsson and Jahn-Takeshi Saito},
  journal={IEEE Transactions on Computational Intelligence and AI in Games},
The success of Monte Carlo tree search (MCTS) in many games, where αβ-based search has failed, naturally raises the question whether Monte Carlo simulations will eventually also outperform traditional game-tree search in game domains where αβ -based search is now successful. The forte of αβ-based search are highly tactical deterministic game domains with a small to moderate branching factor, where efficient yet knowledge-rich evaluation functions can be applied effectively. In this paper, we… CONTINUE READING
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Informed search in complex games

  • M.H.M. Winands
  • Ph.D. dissertation, Dept. Comput. Sci…
  • 2004
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Improving state evaluation, inference, and search in trick-based card games

  • M. Buro, J. R. Long, T. Furtak, N. R. Sturtevant
  • Proc. 21st Int. Joint Conf. Artif. Intell., C…
  • 2009
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