By Topic

A methodology for revealing and monitoring the strategies played by neural networks in mind games

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
$31 $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)
Ghoneim, A.S. ; Sch. of Eng. & Inf. Technol. (SEIT), Univ. of New South Wales, Canberra, ACT, Australia ; Essam, D.L.

An effective approach in the application of computational intelligence to mind-games is neuro-evolution. Neural networks are efficient at autonomous learning and pattern recognition tasks, which has led to many outstanding accomplishments in mind-games' playing ability. Neuro-evolution is a practical way that uses an evolutionary framework to train a neural network in a complicated context where credit assignment is hard. However, the neuro-evolution process may stagnate, or result in solutions with a limited quality. Potentially a cause for this problem are the limitations in understanding the neuro-activities as evolution progresses. A possible solution lies in unfolding the dynamics of the evolution process and the types of the strategies evolved as the evolution progresses; thus providing a diagnostic tool in real-time to identify neuro-dynamic causes of stagnation. Rule-extraction techniques are a notable solution to understanding the networks evolved. However, the extracted rules lack the necessary expressiveness to explain game-playing strategies. We call these rules a syntactic representation of the network that lacks semantic power. In this paper, we will present a methodology whereby a computational environment is used to unfold the evolution of a mind-game neuro-player; thus providing semantic power. Within this environment, we propose to extend the role of computer players to act as a “cognitive” functionality model, thus providing deeper kinds of explanations. We use the game of Go to demonstrate the functionality of the methodology. We then demonstrate that this methodology is successful in determining the types of strategies evolved in a neural Go player, and in monitoring the dynamics of the evolution.

Published in:

Neural Networks (IJCNN), The 2012 International Joint Conference on

Date of Conference:

10-15 June 2012