In this paper, we study human trust and its computational models in supervisory control of swarm robots with varied levels of autonomy (LOA) in a target foraging task. We implement three LOAs: manual, mixed-initiative (MI), and fully autonomous LOA. While the swarm in the MI LOA is controlled by a human operator and an autonomous search algorithm collaboratively, the swarms in the manual and autonomous LOAs are fully directed by the human and the search algorithm, respectively. From user studies, we find that humans tend to make their decisions based on physical characteristics of the swarm rather than its performance since the task performance of swarms is not clearly perceivable by humans. Based on the analysis, we formulate trust as a Markov decision process whose state space includes the factors affecting trust. We develop variations of the trust model for different LOAs. We employ an inverse reinforcement learning algorithm to learn behaviors of the operator from demonstrations where the learned behaviors are used to predict human trust. Compared to an existing model, our models reduce the prediction error by at most 39.6%, 36.5%, and 28.8% in the manual, MI, and auto-LOA, respectively.

中文翻译：

---

人类控制具有不同自主程度的群体的信任模型

在本文中，我们研究了人类信任及其在目标觅食任务中具有不同自主级别（LOA）的群体机器人的监督控制中的计算模型。我们实施了三种 LOA：手动、混合倡议 (MI) 和完全自主的 LOA。MI LOA 中的群由人工操作员和自主搜索算法协同控制，而手动和自主 LOA 中的群分别完全由人类和搜索算法控制。从用户研究中，我们发现人类倾向于根据群体的物理特征而不是其性能做出决定，因为人类无法清楚地感知群体的任务性能。基于分析，我们将信任表述为一个马尔可夫决策过程，其状态空间包括影响信任的因素。我们为不同的 LOA 开发了信任模型的变体。我们采用逆向强化学习算法从演示中学习操作员的行为，其中学习的行为用于预测人类信任。与现有模型相比，我们的模型分别将手动、MI 和自动 LOA 中的预测误差最多降低了 39.6%、36.5% 和 28.8%。