Co-Evolution and Ontogenetic Change in Competing Robots

Abstract

We investigate the dynamics of competitive co-evolution in the framework of two miniature mobile robots, a predator with a vision system and a faster prey with proximity sensors.Both types of robots are controlled by evolutionary neural networks.A variety of efficient chase-escape behaviors emerge in few generations.These results are analyzed in terms of variable fitness landscapes and selection criteria.A new vision of artificial evolution as generation and maintainance of adaptivity is suggested and contrasted with the theory and practice of mainstream evolutionary computation.In a second stage, different types of ontogenetic changes applied to the robot controllers are compared and the results are analyzed in the context of competitive co-evolution.It is shown that predators benefit from forms of directional changes whereas prey attempt to exploit unpredictable behaviors.These results and their effect on coevolutionary dynamics are then considered in relation to open-ended evolution in unpredictably changing environments.