A methodology for learning players' styles from game records
by Mark Levene; Trevor Fenner
International Journal of Artificial Intelligence and Soft Computing (IJAISC), Vol. 2, No. 4, 2011

Abstract: We describe a preliminary investigation into learning a Chess player's style from game records. The method is based on attempting to learn features of a player's individual evaluation function using the method of temporal differences, with the aid of a conventional Chess engine architecture. Some encouraging results were obtained in learning the styles of two Chess world champions, and we report on our attempt to use the learnt styles to discriminate between the players from game records, by trying to detect who was playing white and who was playing black. We also discuss some limitations of our approach.

Online publication date: Tue, 31-Mar-2015

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Artificial Intelligence and Soft Computing (IJAISC):
Login with your Inderscience username and password:

    Username:        Password:         

Forgotten your password?


Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com