Game Programming Gems 2
Putting AI in Entertainment: An AI Authoring Tool for Simulation and Games
IEEE Intelligent Systems
On Multi-Dimensional Encoding/Crossover
Proceedings of the 6th International Conference on Genetic Algorithms
A Neural Network that Learns to Play Five-in-a-Row
ANNES '95 Proceedings of the 2nd New Zealand Two-Stream International Conference on Artificial Neural Networks and Expert Systems
Exploiting Intelligence in Fighting Action Games Using Neural Networks
IEICE - Transactions on Information and Systems
Reinforcement learning of intelligent characters in fighting action games
ICEC'06 Proceedings of the 5th international conference on Entertainment Computing
Adaptation of intelligent characters to changes of game environments
CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part I
Journal of Computer Security
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Recently many studies have attempted to implement intelligent characters for fighting action games. They used genetic algorithms, neural networks, and evolutionary neural networks to create intelligent characters. This study quantitatively compared the performance of these three AI techniques in the same game and experimental environments, and analyzed the results of experiments. As a result, neural network and evolutionary neural network showed excellent performance in the final convergence score ratio while evolutionary neural network and genetic algorithms showed excellent performance in convergence speed. In conclusion, evolutionary neural network which showed excellent results in both the final convergence score ratio and the convergence score is most appropriate AI technique for fighting action games.