TD methods applied to mixture of experts for learning 9/spl times/9 Go evaluation function

The temporal difference (TD) method is applied on a committee of neural network experts to learn the board evaluation function for the oriental board game Go. The game has simple rules but requires complex strategies to play well, and the conventional tree search algorithm for computer games makes a poor Go program. Thus, the game Go is an ideal problem domain for exploring machine learning algorithms. Here, the neural networks learned a board evaluation function for Go played on 9/spl times/9 board sizes. Two learning algorithms, e.g., hybrid mixture of experts (HME) and Meta-Pi, are used to train the neural network experts. Both algorithms learned good Go evaluation functions and the neural network based Go engines were able to defeat a public domain rule-based program more than 50% of the times. The performances of the mixture networks are compared with that of a single feedforward network trained similarly.