Abstract

An investigation into the conditions conducive to the emergence of heterogeneity amoung agents is presented. This is done by using a model of creative artificial agents to investigate some of the possibilities. The simulation is based on Brian Arthur's 'El Farol Bar' model but extended so that the agents also learn and communicate. The learning and communication is implemented using an evolutionary process acting upon a population of strategies inside each agent. This evolutionary learning process is based on a Genetic Programming algorithm. This is chosen to make the agents as creative as possible and thus allow the outside edge of the simulation trajectory to be explored. A detailed case study from the simulations show how the agents have differentiated so that by the end of the run they had taken on qualitatively different roles. It provides some evidence that the introduction of a flexible learning process and an expressive internal representation has facilitated the emergence of this heterogeneity