Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Evolving Neural Networks to Play Go
Applied Intelligence
AI and the Entertainment Industry
IEEE Intelligent Systems
How Qualitative Spatial Reasoning Can Improve Strategy Game AIs
IEEE Intelligent Systems
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
ICEC'07 Proceedings of the 6th international conference on Entertainment Computing
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This paper addresses how intelligent characters, having learning capability based on the neural network technology, automatically adapt to environmental changes in computer games. Our adaptation solution includes an autonomous adaptation scheme and a cooperative adaptation scheme. With the autonomous adaptation scheme, each intelligent character steadily assesses changes of its game environment while taking into consideration recently earned scores, and initiates a new learning process when a change is detected. Intelligent characters may confront various opponents in many computer games. When each intelligent character has fought with just part of the opponents, the cooperative adaptation scheme, based on a genetic algorithm, creates new intelligent characters by composing their partial knowledge of the existing intelligent characters. The experimental results show that intelligent characters can properly accommodate to the changes with the proposed schemes.