Evolving neural network which control a robotic agent

Abstract

Intelligent embodied agents should be able to adopt to changes of the environment and to modify their behavior according to acquired knowledge. The goal of this work is the study of emergence of intelligent behavior within a simple intelligent agent. Cognitive agent functions will be realized by mechanisms based on neural networks of the perceptron type. The adaptation mechanism is realized by the evolutionary algorithms which is responsible for setting the weights of a neural network in a simulated environment. Several tasks including obstacle avoidance and efficient maze exploration are presented in the experimental section. The behaviors developed during the adaptation process compare favorably with hard coded strategies.