Reducing Inter-Agent communication due to negotiation in Multi-Agent systems through Learning
نویسندگان
چکیده
This paper studies the effect that agent learning can have on inter-agent communication in a Multi-agent system. The agents are equipped with MLP neural networks to learn solutions to problems that have been solved through explicit negotiation and communication. We implement a test problem in the form of a Pursuit game, where the MultiAgent system is a set of captor agents. The result is up to 44% fewer negotiation sessions with learning-enabled agents. The importance of learning, in terms of agent knowledge and overall system effectiveness is discussed.
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