Evolving Cooperation of Simple Agents for the Control of an Autonomous Robot

A distributed and scalable architecture for the control of an autonomous robot is presented in this work. In our proposal a whole robotic agent is divided into sub-agents. Every sub-agent is coded into a very simple neural network, and controls one sensor/actuator element of the robot. Sub-agents learn by evolution how to handle their sensor/actuator and how to cooperate with the rest of sub-agents. Emergence of behaviors happens when the co-evolution of several sub-agents embodied into the single robotic agent is produced. It will be demonstrated that the proposed distributed controller learns faster and better than a neuro-evolved central controller.