A Principled Method for Exploiting Opening Books

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 generating 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...

• Type: Journal
• Year: 2010
• Where: CG
• Authors: Romaric Gaudel, Jean-Baptiste Hoock, Julien Perez, Nataliya Sokolovska, Olivier Teytaud
• Keywords: Big Dataset, CG 2010, Computer Graphics, Deterministic Opening Book, Opening Book