n South African Computer Journal - Evolving intelligent game-playing agents : research article
|Article Title||Evolving intelligent game-playing agents : research article|
|© Publisher:||South African Computer Society (SAICSIT)|
|Journal||South African Computer Journal|
|Author||N. Franken and A.P. Engelbrecht|
|Publication Date||Jun 2004|
|Pages||44 - 52|
|Keyword(s)||Artificial intelligence, Co-evolution, Game learning, I.2.6 and Particle swarm optimisation|
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 coevolutionary 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.
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