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首页> 外文会议>IEEE 10th International Conference on Ubiquitous Intelligence and Computing; IEEE 10th International Conference on Autonomic and Trusted Computing >Ranking of Direct Trust, Confidence, and Reputation in an Abstract System with Unreliable Components
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Ranking of Direct Trust, Confidence, and Reputation in an Abstract System with Unreliable Components

机译：具有不可靠成分的抽象系统中的直接信任，信心和声誉排名

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摘要

Trust is an important aspect in human societies. It enables cooperation and provides means to estimate potential cooperation partners. Several works have addressed how the concept of trust can be transferred to computer systems. In this paper, we present an approach to calculate trust, including direct trust, confidence, and reputation, in a network consisting of agents with changing behavior. Our metrics are highly configurable for an adaption to a wide variety of systems and situations, especially Organic Computing Systems can benefit from trust by integrating it in their algorithms implementing self-organizational behavior. We evaluate the effect of direct trust and confidence together with reputation (DTCR) in comparison with using only direct trust (DT) or direct trust with confidence (DTC). Because these metrics can be configured with many parameters leading to an immense number of possible configurations we apply a heuristic optimization algorithm to find very good setups showing the highest benefits. For this evaluation, an abstract scenario is developed and applied, it consists of unreliable components from different classes of defined mean behavior. This general scenario could model many possible industrial settings out of which a few are introduced, too. Our evaluations show that reputation and direct trust are best used together with a fluent transition between them defined by the confidence. In all cases, reputation works as a corrective when direct trust information is not optimal and potentially misleading. This leads to very good results with very limited variance, particularly we show that a small number of interactions are sufficient to obtain the best results.
机译：信任是人类社会的重要方面。它使合作成为可能，并提供了估算潜在合作伙伴的手段。几项工作已经解决了如何将信任的概念转移到计算机系统。在本文中，我们提出了一种在行为发生变化的代理组成的网络中计算信任的方法，包括直接信任，信心和声誉。我们的指标高度可配置，以适应各种系统和情况，尤其是有机计算系统可以通过将信任度集成到实现自组织行为的算法中来从信任中受益。与仅使用直接信任（DT）或直接信任与信任（DTC）相比，我们评估了直接信任和信任以及声誉（DTCR）的效果。由于可以使用许多参数来配置这些度量标准，从而导致大量可能的配置，因此我们应用启发式优化算法来找到显示出最大收益的非常好的设置。为了进行此评估，开发并应用了一种抽象方案，它由来自不同类别的已定义均值行为的不可靠组件组成。这种一般情况可以为许多可能的工业环境建模，其中也引入了一些。我们的评估表明，声誉和直接信任最好与信心所定义的两者之间的流畅过渡一起使用。在所有情况下，当直接信任信息不是最佳的并且可能引起误解时，声誉都可以作为一种纠正措施。这会导致非常好的结果，并且方差非常有限，特别是我们证明了少量的交互足以获得最佳结果。

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