Evolving cooperative neural agents for controlling vision guided mobile robots

We have studied and developed the behavior of two specific neural processes, used for vehicle driving and path planning, in order to control mobile robots. Each processor is an independent agent defined by a neural network trained for a defined task. Through simulated evolution fully trained agents are encouraged to socialize by opening low bandwidth, asynchronous channels between them. Under evolutive pressure agents spontaneously develop communication skills (protolan-guage) that take advantages of interchanged information, even under noisy conditions. The emerged cooperative behavior raises the level of competence of vision guided mobile robots and allows a convenient autonomous exploration of the environment. The system has been tested in a simulated location and shows a robust performance.