부산대학교 도서관

We propose /spl alpha/-/spl 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 /spl alpha/-/spl 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 /spl alpha/-/spl 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 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 /spl alpha/-/spl beta/-evaluation functions efficiently and describe some of the experience we made with this new approach applied to a game called Malawi.ETX