An investigation of an evolutionary approach to the opening of Go

The game of Go can be divided into three stages; the opening, the middle, and the end game. In this paper, evolutionary neural networks, evolved via an evolutionary strategy, are used to develop opening game playing strategies for the game. Go is typically played on one of three different board sizes, i.e., 9/spl times/9, 13/spl times/13 and 19/spl times/19. A 19/spl times/19 board is the standard size for tournament play but 9/spl times/9 and 13/spl times/13 boards are usually used by less-experienced players or for faster games. This work focuses on the opening, using a 13/spl times/13 board. A feed forward neural network player is played against a static player (Gondo), for the first 30 moves. Then Gondo takes the part of both players to play out the remainder of the game. Two experiments are presented which indicate that learning is taking place.