Trust Dynamics: A Case-study on Railway Sensors

Marcin Lenart, A. Bielecki, Marie-Jeanne Lesot, Teodora Petrisor, Adrien Revault d'Allonnes
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引用次数: 1

Abstract

Sensors constitute information providers which are subject to imperfections and assessing the quality of their outputs, in particular the trust that can be put in them, is a crucial task. Indeed, timely recognising a low-trust sensor output can greatly improve the decision making process at the fusion level, help solving safety issues and avoiding expensive operations such as either unnecessary or delayed maintenance. In this framework, this paper considers the question of trust dynamics, i.e. its temporal evolution with respect to the information flow. The goal is to increase the user understanding of the trust computation model, as well as to give hints about how to refine the model and set its parameters according to specific needs. Considering a trust computation model based on three dimensions, namely reliability, likelihood and credibility, the paper proposes a protocol for the evaluation of the scoring method, in the case when no ground truth is available, using realistic simulated data to analyse the trust evolution at the local level of a single sensor. After a visual and formal analysis, the scoring method is applied to real data at a global level to observe interactions and dependencies among
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信任动力学:以铁路传感器为例
传感器是信息的提供者,可能会有缺陷,评估其产出的质量,特别是对它们的信任,是一项至关重要的任务。事实上,及时识别低信任度的传感器输出可以极大地改善融合层面的决策过程,帮助解决安全问题,避免昂贵的操作,如不必要的或延迟的维护。在此框架下,本文考虑了信任动力学问题,即信任随信息流的时间演化。目的是增加用户对信任计算模型的理解,并提示如何根据具体需要改进模型并设置其参数。考虑基于信度、似然和可信性三个维度的信任计算模型,提出了一种评价评分方法的协议,在没有基础真值的情况下,利用真实仿真数据分析单个传感器局部层面的信任演变。在可视化形式化分析之后,将评分方法应用于全局层面的真实数据,观察数据之间的相互作用和依赖关系
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