A pattern classification approach to evaluation function learning

摘要

We present a new approach to evaluation function learning using classical pattern classification methods. Unlike other approaches to game playing where ad hoc methods are used to generate the evaluation function, our approach is a disciplined one based on Bayesian learning. This technique can be applied to any domain where a goal can be defined and an evaluation function can be applied. Such an approach has several advantages: (1) automatic and optimal combination of the features, or terms of the evaluation function; (2) understanding of inter-feature correlation; (3) capability for recovering from erroneous features; and (4) direct estimation of the probability of winning by the evaluation function. We implemented this algorithm using the game of Othello and it resulted in dramatic improvements over a linear evaluation function that has performed at world championship level.