A study in a hybrid centralised-swarm agent community

This paper describes a systems architecture for a hybrid centralised/swarm based multi-agent system. The issue of local goal assignment for agents is investigated through the use of a global agent which teaches the agents responses to given situations. We implement a test problem in the form of a pursuit game, where the multi-agent system is a set of captor agents. The agents learn solutions to certain board positions from the global agent if they are unable to find a solution themselves. The captor agents learn through the use of MLP neural networks. The global agent is able to solve board positions through the use of a genetic algorithm. The cooperation between agents and the results of the simulation are discussed here.