Evolution of Plastic Neurocontrollers for Situated Agents
In this paper we investigate a novel approach to the evolutionary development of autonomous situated agents based on the assumption that the neural mechanisms underlying ontogenetic learning are themselves developed and shaped by evolutionary process. A genetic algorithm is used to evolve neural structures that can be continuously modified during life according to the mechanisms specified in the genotype. The evolutionary process is carried out on a real mobile robot. The analysis of one of the best evolved individuals shows rapid development of stable behavior mediated by fast-changing synapses which are dynamically stable.