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Journal Articles IEEE Computational Intelligence Magazine Year : 2010

Intelligent Agents for the Game of Go


Monte-Carlo Tree Search (MCTS) is a very efficient recent technology for games and planning, par- ticularly in the high-dimensional case, when the number of time steps is moderate and when there is no natural evaluation function. Surprisingly, MCTS makes very little use of learning. In this paper, we present four techniques (ontologies, Bernstein races, Contextual Monte-Carlo and poolRave) for learning agents in Monte-Carlo Tree Search, and experiment them in difficult games and in particular, the game of Go.
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Dates and versions

inria-00544758 , version 1 (08-12-2010)
inria-00544758 , version 2 (12-02-2014)


  • HAL Id : inria-00544758 , version 2


Jean-Baptiste Hoock, Chang-Shing Lee, Arpad Rimmel, Fabien Teytaud, Olivier Teytaud, et al.. Intelligent Agents for the Game of Go. IEEE Computational Intelligence Magazine, 2010. ⟨inria-00544758v2⟩
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