In this paper, we develop a Bayesian inference model for the degree of human trust in multiple mobile robots. A linear model for robot performance in navigation and perception is first devised. We then propose a computational trust model for the human multi-robot team based on a dynamic Bayesian network (DBN). In the trust DBN, the robot performance is the network input, the human trust feedback to each individual robot and the human intervention are the outputs (observations). The categorical Boltzmann machine is used to capture the multinomial distributions that model the conditional dependencies of the DBN. We introduce the expectation maximization (EM) algorithm for the model learning and personalization. A factorial form of the EM algorithm is adopted for the multi-robot system where each robot has its corresponding latent trust state in the human mind. Bayesian inference is conducted to find the trust states, i.e., the trust belief. Based on the inferred trust states, we further derive the formulation to predict human interventions for model validation. A simulated human-UAV collaborative search mission is conducted with humans-in-the-loop. The experiment results show the Bayesian trust inference model can infer the degrees of human trust in multiple mobile robots and also predict human interactions with relatively high accuracy (72.2\(\%\)). These findings confirm the effectiveness of DBNs in modeling human trust towards multi-robot systems.

在本文中，我们针对多个移动机器人中的人类信任程度开发了贝叶斯推理模型。首先设计了用于导航和感知中的机器人性能的线性模型。然后，我们基于动态贝叶斯网络（DBN）为人类多机器人团队提出了一种计算信任模型。在信任DBN中，机器人性能是网络输入，对每个单个机器人的人类信任反馈和人类干预是输出（观察）。绝对Boltzmann机器用于捕获对DBN的条件依赖性建模的多项式分布。我们介绍了用于模型学习和个性化的期望最大化（EM）算法。EM算法的阶乘形式适用于多机器人系统，其中每个机器人在人脑中都有其对应的潜在信任状态。进行贝叶斯推断以找到信任状态，即信任信念。基于推断的信任状态，我们进一步推导公式以预测模型验证的人为干预。在环人类中进行模拟的人类-UAV协同搜索任务。实验结果表明，贝叶斯信任推理模型可以推断多个移动机器人中的人类信任程度，并且可以相对较高的精度预测人类的互动（72.2 在环人类中进行模拟的人类-UAV协同搜索任务。实验结果表明，贝叶斯信任推理模型可以推断多个移动机器人中的人类信任程度，并且可以相对较高的精度预测人类的互动（72.2 在环人类中进行模拟的人类-UAV协同搜索任务。实验结果表明，贝叶斯信任推理模型可以推断多个移动机器人中的人类信任程度，并且可以相对较高的精度预测人类的互动（72.2\（\％\））。这些发现证实了DBN在建模人类对多机器人系统的信任方面的有效性。