Complex environments, complex behaviour

A convincing argument has been made in the literature for the advantages gained by using modular neural networks (NN) instead of homogeneous structures. Here, a modular NN design was used in conjunction with evolutionary algorithm methods to evolve an animat capable of learning behavioural patterns at several levels of complexity. A parallel was drawn between the training of the animat and the stages of learning experienced by the young of many animals. Following movement learning, the animat was eventually capable of navigating an environment and avoiding obstacles. Discussion is made of how such an animat with many degrees of freedom can develop complex behavioural patterns. (C) 2004 Elsevier Ltd. All rights reserved.