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提出了一种 Agent行为规范的实现机制 ,讨论了规范可获收益的下界 ,并给出了基于人工神经网络学习来解决行为规范问题的结构 .行为规范产生机制考虑了 Agent对规范制定影响力的差异 ,体现了规范作为连接宏观和微观的纽带作用 ;行为规范机制具有动态性 ,可以随系统运行而不断演化 .同相关工作相比 ,考虑了 Agent的社会性差异和规范的强制与进化的特征
This paper proposes a realization mechanism of Agent’s behavior specification, discusses the lower bound of the available benefits of regulation, and gives the structure that solves the problem of behavior specification based on the learning of artificial neural network.The generation mechanism of behavior criterion considers the influence of Agent on the influence Difference reflects the role of norms as a link between the macroscopic and the microcosmic. The behavioral norms mechanism is dynamic and can evolve with the operation of the system. Compared with the related work, the social differences of agents and the characteristics of the norms of coercion and evolution are considered