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Trust Evaluation through Human-Machine Dialogue Modelling

机译:通过人机对话建模进行信任评估

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Trust in automation, and particularly maintaining an adequate level of trust in automation is now recognized as a major performance factor in supervisory control. Leveraging mem-machine interaction is seen as a promising approach to influence the level of trust of an operator. Two problems need to be addressed in order to reach this goal: first measuring the level of trust; second acting on the level of trust to reach a more appropriate level. In this paper, we tackle the first problem, and propose to use a computational dialogue modelling approach to evaluate trust dynamically. We describe our model on two examples and give some perspectives.
机译:对自动化的信任,尤其是保持对自动化的充分信任,现在被认为是监督控制的主要绩效因素。利用内存机交互被认为是影响操作员信任度的一种有前途的方法。为了实现这一目标,需要解决两个问题:第一,衡量信任度;第二,评估信任度。第二,在信任水平上行事达到更适当的水平。在本文中,我们解决了第一个问题,并提出使用计算对话建模方法来动态评估信任。我们通过两个示例描述我们的模型,并给出一些观点。

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