A Principled Method for Exploiting Opening Books

Romaric Gaudel 1 Jean-Baptiste Hoock 1, 2 Julien Pérez 1 Nataliya Sokolovska 2 Olivier Teytaud 1, 2
2 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 : We used in the past a lot of computational power and human expertise for having a very big dataset of good 9x9 Go games, in order to build an opening book. We improved a lot the algorithm used for gen- erating these games. Unfortunately, the results were not very robust, as (i) opening books are definitely not transitive, making the non-regression testing extremely difficult and (ii) different time settings lead to opposite conclusions, because a good opening for a game with 10s per move on a single core is very different from a good opening for a game with 30s per move on a 32-cores machine (iii) some very bad moves sometimes occur. In this paper, we formalize the optimization of an opening book as a matrix game, compute the Nash equilibrium, and conclude that a naturally randomized opening book provides optimal performance (in the sense of Nash equilibria); surprisingly, from a finite set of opening books, we can choose a distribution on these opening books so that this random solution has a significantly better performance than each of the deterministic opening book.
Type de document :
Communication dans un congrès
International Conference on Computers and Games, Sep 2010, Kanazawa, Japan. 2010
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Romaric Gaudel, Jean-Baptiste Hoock, Julien Pérez, Nataliya Sokolovska, Olivier Teytaud. A Principled Method for Exploiting Opening Books. International Conference on Computers and Games, Sep 2010, Kanazawa, Japan. 2010. 〈inria-00484043〉

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