Studies in artificial evolution
Studies in artificial evolution
Adaptive individuals in evolving populations
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
Co-ordination in Multi-Agent Systems
Software Agents and Soft Computing: Towards Enhancing Machine Intelligence, Concepts and Applications
Evolution of behaviors in autonomous robot using artificial neural network and genetic algorithm
Information Sciences: an International Journal
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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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The following paper introduces an evolution strategy on the basis of cooperative behaviors in each group of agents. The evolution strategy helps each agent to be self-defendable and self-maintainable. To determine an optimal group behavior strategy under dynamically varying circumstances, agents in same group cooperate with each other. This proposed method use reinforcement learning, enhanced neural network, and artificial life. In the present paper, we apply two different reward models: reward model 1 and reward model 2. Each reward model is designed as considering the reinforcement or constraint of behaviors. In competition environments of agents, the behavior considered to be advantageous is reinforced as adding reward values. On the contrary, the behavior considered to be disadvantageous is constrained as subtracting the values. And we propose an enhanced neural network to add learning behavior of an artificial organism-level to artificial life simulation. In future, the system models and results described in this paper will be applied to the framework of healthcare systems that consists of biosensors, healthcare devices, and healthcare system.