Trust has been identified as a central factor for effective human-robot teaming. Existing literature on trust modeling predominantly focuses on dyadic human-autonomy teams where one human agent interacts with one robot. There is little, if not no, research on trust modeling in teams consisting of multiple human agents and multiple robotic agents. To fill this research gap, we present the trust inference and propagation (TIP) model for trust modeling in multi-human multi-robot teams. In a multi-human multi-robot team, we postulate that there exist two types of experiences that a human agent has with a robot: direct and indirect experiences. The TIP model presents a novel mathematical framework that explicitly accounts for both types of experiences. To evaluate the model, we conducted a human-subject experiment with 15 pairs of participants (${N=30}$). Each pair performed a search and detection task with two drones. Results show that our TIP model successfully captured the underlying trust dynamics and significantly outperformed a baseline model. To the best of our knowledge, the TIP model is the first mathematical framework for computational trust modeling in multi-human multi-robot teams.

翻译：信任已被认定为有效人机团队协作的核心因素。现有信任建模研究主要聚焦于单人类主体与单机器人交互的二元人机自主协作团队，而对包含多人类主体与多机器人主体的团队信任建模研究鲜有涉及（近乎空白）。为填补这一研究空白，我们提出面向多人类多机器人团队的信任推理与传播(TIP)模型。在多人类多机器人团队中，我们假设人类主体对机器人存在两类体验：直接体验与间接体验。TIP模型提出了一个新颖的数学框架，明确纳入了这两类体验。为评估该模型，我们开展了包含15对参与者（N=30）的人机实验。每对参与者需与两架无人机协作完成搜索检测任务。结果表明，我们的TIP模型成功捕捉了潜在的信任动态，且显著优于基线模型。据我们所知，TIP模型是首个面向多人类多机器人团队计算信任建模的数学框架。