Evolving intelligent game-playing agents

Authors:
Nelis Franken;Andries P. Engelbrecht
Affiliations:
Department of Computer Science, University of Pretoria, Pretoria, 0002, South Africa;Department of Computer Science, University of Pretoria, Pretoria, 0002, South Africa
Venue:
SAICSIT '03 Proceedings of the 2003 annual research conference of the South African institute of computer scientists and information technologists on Enablement through technology
Year:
2003

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Abstract

Traditional game playing programs have relied on advanced search algorithms and hand-tuned evaluation functions to play 'intelligently'. A historical overview of these techniques is provided, followed by a revealing look at recent developments in co-evolutionary strategies to facilitate game learning. The use of particle swarms in conjunction with neural networks to learn how to play tic-tac-toe is experimentally compared to current game learning research. The use of a new particle swarm neighbourhood structure and innovative board state representation show promising results that warrant further investigation to its application in more complex games like checkers.