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On the Huge Benefit of Decisive Moves in Monte-Carlo Tree Search Algorithms

Fabien Teytaud 1, 2 Olivier Teytaud 1, 2
1 TAO - Machine Learning and Optimisation
CNRS - Centre National de la Recherche Scientifique : UMR8623, Inria Saclay - Ile de France, UP11 - Université Paris-Sud - Paris 11, LRI - Laboratoire de Recherche en Informatique
Abstract : Monte-Carlo Tree Search (MCTS) algorithms, including upper confidence Bounds (UCT), have very good results in the most difficult board games, in particular the game of Go. More recently these methods have been successfully introduce in the games of Hex and Havannah. In this paper we will define decisive and anti-decisive moves and show their low computational overhead and high efficiency in MCTS.
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Submitted on : Friday, June 25, 2010 - 8:50:33 AM
Last modification on : Thursday, July 8, 2021 - 3:47:42 AM
Long-term archiving on: : Monday, September 27, 2010 - 11:55:43 AM


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  • HAL Id : inria-00495078, version 1



Fabien Teytaud, Olivier Teytaud. On the Huge Benefit of Decisive Moves in Monte-Carlo Tree Search Algorithms. IEEE Conference on Computational Intelligence and Games, Aug 2010, Copenhagen, Denmark. ⟨inria-00495078⟩



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