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The Grand Challenge of Computer Go: Monte Carlo Tree Search and Extensions

Sylvain Gelly 1, 2 Levente Kocsis 3 Marc Schoenauer 4, 1 Michèle Sebag 1, 2 David Silver 5 Csaba Szepesvari 6 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 : The ancient oriental game of Go has long been considered a grand challenge for artificial intelligence. For decades, com- puter Go has defied the classical methods in game tree search that worked so successfully for chess and checkers. How- ever, recent play in computer Go has been transformed by a new paradigm for tree search based on Monte-Carlo meth- ods. Programs based on Monte-Carlo tree search now play at human-master levels and are beginning to challenge top professional players. In this paper we describe the leading algorithms for Monte-Carlo tree search and explain how they have advanced the state of the art in computer Go.
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Contributor : Olivier Teytaud <>
Submitted on : Tuesday, May 8, 2012 - 6:54:50 AM
Last modification on : Wednesday, December 11, 2019 - 5:10:02 PM
Document(s) archivé(s) le : Friday, November 30, 2012 - 11:25:23 AM


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  • HAL Id : hal-00695370, version 1


Sylvain Gelly, Levente Kocsis, Marc Schoenauer, Michèle Sebag, David Silver, et al.. The Grand Challenge of Computer Go: Monte Carlo Tree Search and Extensions. Communications- ACM, Association for Computing Machinery, 2012, 55 (3), pp.106-113. ⟨hal-00695370v1⟩



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