Bootstrap learning of α-β-evaluation functions

We propose α-β-evaluation functions that can be used in game-playing programs as a substitute for the traditional static evaluation functions without loss of functionality. The main advantage of an α-β-evaluation function is that it can be implemented with a much lower time complexity than the traditional counterpart and so provides a significant speedup for the evaluation of any game position which eventually results in better play. We describe an implementation of the α-β-evaluation function using a modification of the classical classification and regression trees and show that a typical call to this function involves the computation of only a small subset of all features that may be used to describe a game position. We show that an iterative bootstrap process con be used to learn α-β-evaluation functions efficiently and describe some of the experience we made with this new approach applied to a game called Malawi