A parallel network that learns to play backgammon

摘要

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. These include techniques for intelligent design of training set examples and efficient coding schemes, and procedures for escaping from local minima. We suggest how these techniques might be used in applications of network learning to general large-scale, difficult “real-world” problem domains.