Evolutionary patterns of agent organizations

Problems approached by multiagent systems (MAS) are typically complex. It is usually difficult to know at system design stage how many agents need to be in the system, what each agent's role is, and how the agents should interact to get optimal performance out of the group. The aim of the testbed presented here is to investigate which kinds of multiagent systems could be developed to solve ranges of problems, avoiding the need to reorganize the agents from scratch for each task. The agent organization process explored here is based on the agents' knowledge, and not on their tasks. This opens up a new approach for Distributed Artificial Intelligence designers, to have their domain organized before the allocation of tasks among agents. These kinds of organizations should be more robust for solving different problems related to the same knowledge. We define information oriented domains (IODs) for that purpose. An evolutionary approach to the design of a multiagent system is suggested. Our model is based on a cellular automaton whose rules of dynamics induce the formation of an organization of agents. Patterns of organization obtained empirically are presented. Our knowledge-based organization approach is analyzed both from theoretical and practical perspectives.