A new training paradigm, called the "comparison paradigm," is introduced for tasks in which a network must learn to choose a preferred pattern from a set of n alternatives, based on examples of human expert preferences. In this paradigm, the input to the network consists of two of the n alternatives, and the trained output is the expert's judgement of which pattern is better. This paradigm is applied to the learning of backgammon, a difficult board game in which the expert selects a move from a set of legal moves. With comparison training, much higher levels of performance can be achieved, with networks that are much smaller, and with coding schemes that are much simpler and easier to understand. Furthermore, it is possible to set up the network so that it always produces consistent rank-orderings.
['Gerald Tesauro']
Connectionist Learning of Expert Preferences by Comparison Training