Title: Bootstrap learning of α-β-evaluation functions
Abstract: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...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 xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>Read More
Publication Year: 2002
Publication Date: 2002-12-30
Language: en
Type: article
Indexed In: ['crossref']
Access and Citation
AI Researcher Chatbot
Get quick answers to your questions about the article from our AI researcher chatbot