Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Incremental evolution of complex general behavior
Adaptive Behavior - Special issue on environment structure and behavior
Learning cases to resolve conflicts and improve group behavior
International Journal of Human-Computer Studies - Evolution and learning in multiagent systems
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Distributed Autonomous Robotic Systems
Distributed Autonomous Robotic Systems
Multiagent Systems: A Survey from a Machine Learning Perspective
Autonomous Robots
Cooperative Multiagent Systems: A Personal View of the State of the Art
IEEE Transactions on Knowledge and Data Engineering
An Artificial Neural Network Representation for Artificial Organisms
PPSN I Proceedings of the 1st Workshop on Parallel Problem Solving from Nature
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Socially intelligent reasoning for autonomous agents
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Fuzzy CoCo: a cooperative-coevolutionary approach to fuzzy modeling
IEEE Transactions on Fuzzy Systems
An Evolutionary Solution for Cooperative and Competitive Mobile Agents
AICI '09 Proceedings of the International Conference on Artificial Intelligence and Computational Intelligence
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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.