An Evaluation of Two Alternatives to Minimax

TL;DR

This paper evaluates alternative algorithms to minimax in game AI, showing that some new methods outperform minimax by better utilizing evaluation functions based on experimental results.

Contribution

It introduces and assesses new algorithms that improve upon minimax in game decision-making by more effectively leveraging evaluation functions.

Findings

New algorithms outperform minimax in model games

Evaluation functions are utilized more effectively by the new methods

Experimental results demonstrate significant improvements

Abstract

In the field of Artificial Intelligence, traditional approaches to choosing moves in games involve the we of the minimax algorithm. However, recent research results indicate that minimizing may not always be the best approach. In this paper we summarize the results of some measurements on several model games with several different evaluation functions. These measurements, which are presented in detail in [NPT], show that there are some new algorithms that can make significantly better use of evaluation function values than the minimax algorithm does.