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A Multiagent Cooperation Model Based on Trust Rating in Dynamic Infinite Interaction Environment

机译：动态无限交互环境下基于信任度的多主体合作模型

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摘要

To improve the liveness of agents and enhance trust and collaboration in multiagent system, a new cooperation model based on trust rating in dynamic infinite interaction environment (TR-DII) is proposed. TR-DII model is used to control agent's autonomy and selfishness and to make agent do the rational decision. TR-DII model is based on two important components. One is dynamic repeated interaction structure, and the other is trust rating. The dynamic repeated interaction structure is formed with multistage inviting and evaluating actions. It transforms agents' interactions into an infinity task allocation environment, where controlled and renewable cycle is a component most agent models ignored. Additionally, it influences the expectations and behaviors of agents which may not appear in one-shot time but may appear in long-time cooperation. Moreover, with rewards and punishments mechanism (RPM), the trust rating (TR) is proposed to control agent blindness in selection phase. However, RPM is the factor that directly influences decisions, not the reputation as other models have suggested. Meanwhile, TR could monitor agent's statuses in which they could be trustworthy or untrustworthy. Also, it refines agent's disrepute in a new way which is ignored by the others. Finally, grids puzzle experiment has been used to test TR-DII model and other five models are used as comparisons. The results show that TR-DII model can effectively adjust the trust level between agents and makes the solvers be more trustworthy and do choices that are more rational. Moreover, through interaction result feedback, TR-DII model could adjust the income function, to control cooperation reputation, and could achieve a closed-loop control.
机译：为了提高Agent的生存能力，增强多Agent系统中的信任与协作，提出了一种基于信任的动态无限交互环境（TR-DII）合作模型。 TR-DII模型用于控制代理人的自主性和自私性，并使代理人做出理性的决定。 TR-DII模型基于两个重要组成部分。一种是动态的重复交互结构，另一种是信任等级。动态的重复交互结构是由多阶段的邀请和评估动作形成的。它将座席的交互转换为无限任务分配环境，其中受控和可更新的周期是大多数座席模型忽略的组件。此外，它会影响代理的期望和行为，这些期望和行为可能不会一次出现，而可能会出现在长期合作中。此外，通过奖惩机制（RPM），提出了信任等级（TR）来控制代理商选择阶段的失明。但是，RPM是直接影响决策的因素，而不是其他模型所建议的声誉。同时，TR可以监视代理人的状态，使他们处于可信任或不可信任状态。而且，它以一种新的方式完善了代理人的声誉，而其他人则忽略了这种方式。最后，网格拼图实验已用于测试TR-DII模型，而其他五个模型用作比较。结果表明，TR-DII模型可以有效地调整代理之间的信任度，使求解器更加可信，可以做出更合理的选择。此外，通过交互结果反馈，TR-DII模型可以调整收益函数，控制合作声誉，并可以实现闭环控制。

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  • 来源
    《Mathematical Problems in Engineering》 |2018年第6期|2089596.1-2089596.11|共11页
  • 作者

    Fan Sixia;

  • 作者单位

    Shanghai Dianji Univ, Sch Business, Shanghai 201306, Peoples R China;

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  • 正文语种 eng
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