Trust in automation, or more recently trust in autonomy, has received extensive research attention in the past three decades. The majority of prior literature adopted a “snapshot” view of trust and typically evaluated trust through questionnaires administered at the end of an experiment. This “snapshot” view, however, does not acknowledge that trust is a dynamic variable that can strengthen or decay over time. To fill the research gap, the present study aims to model trust dynamics when a human interacts with a robotic agent over time. The underlying premise of the study is that by interacting with a robotic agent and observing its performance over time, a rational human agent will update his/her trust in the robotic agent accordingly. Based on this premise, we develop a personalized trust prediction model and learn its parameters using Bayesian inference. Our proposed model adheres to three properties of trust dynamics characterizing human agents’ trust development process de facto and thus guarantees high model explicability and generalizability. We tested the proposed method using an existing dataset involving 39 human participants interacting with four drones in a simulated surveillance mission. The proposed method obtained a root mean square error of 0.072, significantly outperforming existing prediction methods. Moreover, we identified three distinct types of trust dynamics, the Bayesian decision maker, the oscillator, and the disbeliever, respectively. This prediction model can be used for the design of individualized and adaptive technologies.

在过去的三十年中，对自动化的信任，或更近来对自治的信任，受到了广泛的研究关注。大多数现有文献都采用“快照”式的信任观，通常通过在实验结束时进行问卷调查来评估信任度。但是，这种“快照”视图并不承认信任是可以随着时间的流逝而增强或减弱的动态变量。为了填补研究空白​​，本研究旨在对人类随着时间的推移与机器人代理进行交互时的信任动态进行建模。该研究的基本前提是，通过与机器人代理进行交互并随时间观察其性能，理性的人类代理将相应地更新其对机器人代理的信任。基于这个前提，我们开发了个性化的信任预测模型，并使用贝叶斯推理来学习其参数。我们提出的模型遵循信任动力学的三个特性，这些特性描述了人类代理人的信任发展过程实际上，因此可以保证较高的模型可解释性和可概括性。我们使用现有的数据集测试了该方法，该数据集包含39名人类参与者与模拟监视任务中的四架无人机进行交互。所提出的方法的均方根误差为0.072，大大优于现有的预测方法。此外，我们分别确定了三种不同的信任动态类型，即贝叶斯决策者，振荡者和不信者。该预测模型可用于设计个性化和自适应技术。