Abstract
As an effective means of multi-agent problem solving, autonomous individual programming and interaction that are essential need, are limited in their ability to accommodate the interests of others, and therefore, may unnecessarily constrain the solving ability and negotiability of an agent, particularly in a distributed cooperative environments founded on private and uncertain information. In this paper, a multi-agent group programming model is presented, where each agent executes local programming by evolutionary search. Based on co-evolution idea, agents resolve conflicts and revise their own search direction to optimize local and social objectives in an interactive process by means of clustering and group choice. Finally, the paper presents simulation results that illustrate the operational effectiveness of our agent group programming model.
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