Generation of fish school by multi agent autonomous learning algorithm

In this paper, we propose the agent-based architecture to generate fish behaviors in Visual Marine Museum. We use immune network as a decentralized consensus-making system for the behavior arbitration. Parameters of the immune network are automatically acquired by a particle swarm optimization algorithm. Through some numerical experiments, it is observed that appropriate behaviors can be selected by the proposed algorithm.