Abstract
In engineering aspects, the goal of artificial life is to incarnate unique behaviors or phenomena of living creatures in nature into artifacts like computers. Artificial life can provide a useful methodology for multiple mobile agent learning which is full of autonomy and creativity. In this paper, a neural network is used for the behavior decision controller. The input of the neural network is decided by the existence of other agents and the distance to the other agents. The output determines the directions in which the agent moves. The connection weight values of this neural network are encoded as genes, and the fitness of individuals is determined using a genetic algorithm. Here, the fitness values imply how much group behaviors fit adequately to the goal. The validity of the system is verified through simulation. Moreover, in this paper, we could have observed the agents' emergent behaviors during simulation.
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Lee, M., Chang, OB., Yoo, CJ. et al. Behavior Evolution of Multiple Mobile Agents under Solving a Continuous Pursuit Problem Using Artificial Life Concept. Journal of Intelligent and Robotic Systems 39, 433–445 (2004). https://doi.org/10.1023/B:JINT.0000026083.64909.e4
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DOI: https://doi.org/10.1023/B:JINT.0000026083.64909.e4