Analysis of emergent properties in a hybrid bio-inspired architecture for cognitive agents

Abstract

In this work, a hybrid, self-configurable, multilayered and evolutionary architecture for cognitive agents is developed. Each layer of the subsumption architecture is modeled by one different Machine Learning System MLS based on bio-inspired techniques. In this research an evolutionary mechanism supported on Gene Expression Programming to self-configure the behaviour arbitration between layers is suggested. In addition, a co-evolutionary mechanism to evolve behaviours in an independent and parallel fashion is used. The proposed approach was tested in an animat environment using a multi-agent platform and it exhibited several learning capabilities and emergent properties for self-configuring internal agent's architecture. © 2007 Springer-Verlag Berlin Heidelberg.