Search versus Knowledge: An Empirical Study of Minimax on KRK

This article presents the results of an empirical experiment designed to gain insight into what is the effect of the minimax algorithm on the evaluation function. The experiment’s simulations were performed upon the KRK chess endgame. Our results show that dependencies between evaluations of sibling nodes in a game tree and an abundance of possibilities to commit blunders present in the KRK endgame are not sufficient to explain the success of the minimax principle in practical game-playing as was previously believed. The article shows that minimax in combination with a noisy evaluation function introduces a bias into the backed-up evaluations and argues that this bias is what masked the effectiveness of the minimax in previous studies.

• Type: Conference
• Year: 2003
• Where: ACG
• Authors: Aleksander Sadikov, Ivan Bratko, Igor Kononenko
• Keywords: ACG 2003, Computer Graphics, KRK Chess Endgame, Minimax, Minimax Algorithm