Technical Report No. 48, January 1994 - Abstract

Alois P. Heinz, Christoph Hense:
Bootstrap Learning of alpha-beta-Evaluation Functions

We propose alpha-beta-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 alpha-beta-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 alpha-beta-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 asmall subset of all features that may be used to describe a game position.

We show that an iterative bootstrap process can be used to learn alpha-beta-evaluation functions efficiently and describe some of the experience we made with this new approach applied to a game called malawi.