A survey of the application of machine learning to the game of go
Jan Ramon, Hendrik Blockeel · Lirias · 2001
Unlike other games such as chess, draughts and backgammon, computers are currently quite weak at the game of go (baduk). Brute force is dicult due to the higher branching factor and game length. Human made algorithms become very complex before even approaching human strength on a subproblem of the game. One possible approach to this challenging problem is to use machine learning to let the program learn and improve without increased human eort. Machine learning has been successful in other games (e.g. draughts, backgammon). In this paper we give an overview of existing techniques. We discuss dierent aspects of learning, and propose some directions of research. In particular we believe that a rst order representation language combined with a multistrategy learning system can achieve much more than what currently exists.