A pattern classification approach to evaluation function learning
type: Item
instance of: scholarly article
title: A pattern classification approach to evaluation function learning
zbMATH Open document ID: 0644.68111
DOI: 10.1016/0004-3702(88)90076-8
author: Kai-Fu Lee
author: Sanjoy Mahajan
published in: Artificial Intelligence
publication date: 1988-01-01T00:00:00Z
full work available at URL: https://figshare.com/articles/journal_contribution/A_pattern_classification_approach_to_evaluation_function_learning/6591158
review text: We present a new approach to evaluation function learning using classical pattern classification methods. Unlike other approaches to game playing where ad hoc methods are used to generate the evaluation function, our approach is a disciplined one based on Bayesian learning. This technique can be applied to any domain where a goal can be defined and an evolution function can be applied. Such an approach has several advantages: (1) automatic and optimal combination of the features, or terms of the evaluation function; (2) understanding of inter-feature correlation; (3) capability for recovering from erroneous features; and (4) direct estimation of the probability of winning by the evaluation function. We implemented this algorithm using the game of Othello and it resulted in dramatic improvements over a linear evaluation function that has performed at world championship level.
Mathematics Subject Classification ID: 68T05
Mathematics Subject Classification ID: 68T10
Mathematics Subject Classification ID: 62H30
Mathematics Subject Classification ID: 91A40
zbMATH DE Number: 4051040
zbMATH Keywords: pattern classification
zbMATH Keywords: game playing
zbMATH Keywords: Bayesian learning
zbMATH Keywords: evolution function
zbMATH Keywords: Othello
MaRDI profile type: Publication
OpenAlex ID: W2016616384
cites work: Q4403756
cites work: A comparison and evaluation of three machine learning procedures as applied to the game of checkers
cites work: A world-championship-level Othello program
description: scientific article; zbMATH DE number 4051040