A Study in a Hybrid Centralised-Swarm Agent Community

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dc.creatorvan Aardt, Bradley
dc.creatorMarwala, Tshilidzi
dc.date2007-05-16
dc.date.accessioned2026-07-07T08:01:49Z
dc.date.available2026-07-07T08:01:49Z
dc.descriptionThis 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. The captor agents learn through the use of multi-layer perceptron 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. .
dc.description6 pages
dc.identifierhttps://arxiv.org/abs/0705.2307
dc.identifierhttp://arxiv.org/abs/0705.2307
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/129037
dc.subjectNeural and Evolutionary Computing
dc.subjectArtificial Intelligence
dc.titleA Study in a Hybrid Centralised-Swarm Agent Community
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