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)

1 LRI - Laboratoire de Recherche en Informatique
2 TAO - Machine Learning and Optimisation

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.

Mots clés: Opening book; Nash equilibrium; Fictitious Play; Go

Domaines: Apprentissage [cs.LG]

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. ⟨inria-00484043⟩