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Towards a cognitive meta-model for adaptive trust and reputation in open multi-agent systems

机译:面向开放式多代理系统中适应性信任和声誉的认知元模型

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Computational trust and reputation models are key elements in the design of open multi-agent systems. They offer means of evaluating and reducing risks of cooperation in the presence of uncertainty. However, the models proposed in the literature do not consider the costs they introduce and how they are affected by environmental aspects. In this paper, a cognitive meta-model for adaptive trust and reputation in open multi-agent systems is presented. It acts as a complement to a non-adaptive model by allowing the agent to reason about it and react to changes in the environment. We demonstrate how the meta-model can be applied to existent models proposed in the literature, by adjusting the model’s parameters. Finally, we propose evaluation criteria to drive meta-level reasoning considering the costs involved when employing trust and reputation models in dynamic environments.
机译:计算信任和信誉模型是开放式多代理系统设计中的关键要素。它们提供了在存在不确定性的情况下评估和降低合作风险的方法。但是,文献中提出的模型没有考虑它们引入的成本以及它们如何受到环境因素的影响。本文提出了一种在开放式多智能体系统中用于自适应信任和声誉的认知元模型。它通过允许代理对它进行推理并对环境的变化做出反应,从而对非自适应模型起到了补充作用。我们演示了如何通过调整模型的参数将元模型应用于文献中提出的现有模型。最后,考虑到在动态环境中使用信任和信誉模型时所涉及的成本,我们提出了评估标准,以驱动元级推理。

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