The game of Go is an ancient Chinese game of strategy for two players. By most measures of complexity it is more complex than Chess. While Deep Blue (and more recently Deep Fritz) play Chess at the world champion's level no Go-playing program has yet even reached the level of play of an average amateur Go player. The reason for the failure to reproduce the impressive results in chess for the game of Go appear to lie in its greater complexity, both in terms of the number of different positions and in the difficulty of defining an appropriate evaluation function for Go positions.

We take the view, that only an automated way of acquiring Go knowledge - machine learning - can radically improve on the current situation in computer Go. Numerous Go servers in the internet offer thousands of game records of Go played by players that are very competent as compared to today's computer Go programs. The great challenge is to build machine learning algorithms that extract knowledge from these data-bases such that it can be used for playing Go well.