By Topic

Overcoming Alpha-Beta Limitations Using Evolved Artificial Neural Networks

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Yarin Gal ; Dept. of Math. & Comput. Sci., Open Univ. of Israel, Ra'anana, Israel ; Mireille Avigal

In order to give the computer the ability to play against human opponents, one could utilize the Alpha-Beta algorithm. However, this algorithm has several limitations restricting its playing capabilities. Over the years, many variants of this algorithm were developed, among them a couple that make use of neural networks: a neural network to focus the search in the game tree, and a neural network trained without expert knowledge that substitutes the heuristic function in the Alpha-Beta algorithm. In this paper the weaknesses of the Alpha-Beta algorithm are reviewed alongside its variants that use neural networks. It is explained how each approach overcomes different limitations of the Alpha-Beta algorithm, and an attempt to overcome its weaknesses by the use of a combination of the neural network algorithms is presented. The proposed hybrid algorithm, which was developed using Evolutionary Strategies, still keeps the advantages of each of the individual neural algorithms, and shows a significant improvement in play against them.

Published in:

Machine Learning and Applications (ICMLA), 2010 Ninth International Conference on

Date of Conference:

12-14 Dec. 2010