TIle networks were trained by a supervised learning procedure on a large set of sample positions evaluated by a human expert. In actual match play against humans and conventional computer programs, the networks demonstrate substantial ability to generalize on the basis of expert knowledge. Our study touches on some of the most important issues in net(cid:173) work learning theory, including the development of efficient coding schemes and training procedures, scaling, generalization, the use of real-valued inputs and out(cid:173) puts, and techniques for escaping from local minima. Practical applications in games and other domains are also discussed.

A class of connectionist networks is described that has learned to play backgammon at an intermediate-to-advanced level. The networks were trained by back-propagation learning on a large set of sample positions evaluated by a human expert. In actual match play against humans and conventional computer programs, the networks have demonstrated substantial ability to generalize on the basis of expert knowledge of the game. This is possibly the most complex domain yet studied with connectionist learning. New techniques were needed to overcome problems due to the scale and complexity of the task.