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联盟形成是多智能体系统中一个重要的协同问题,对智能体的协同能力进行适当的描述是处理这个问题的一个基本且必要的前提。这篇文章对智能体的协同能力进行了建模,该模型由五个影响因素构成。同时,对任务需求向量进行了描述。提了一种随机机制以减少联盟形成过程中的过度竞争。此外,为了减少任务需求和实际任务需求之间的差距,提出了一种人工智能方法,该方法可以提高多智能体对人类指令的认知。实验结果显示了该模型及分布式人工智能方法的有效性。
Coalition formation is an important synergetic problem in multi-agent system. Proper description of agent’s synergistic ability is a basic and necessary precondition to deal with this problem. This article models the interoperability of an agent, which consists of five influencing factors. At the same time, the task demand vector is described. A stochastic mechanism is proposed to reduce over-competition in the formation of coalitions. In addition, in order to reduce the gap between mission requirements and actual mission requirements, an artificial intelligence method is proposed, which can improve the multi-agent cognition of human instructions. The experimental results show the effectiveness of this model and the distributed artificial intelligence approach.