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：信任已被确认为人机团队协作有效性的核心因素。现有信任建模文献主要聚焦于单人类智能体与单机器人交互的二元人机自主团队，而针对多人类智能体与多机器人智能体构成团队的信任建模研究几乎空白。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断与传播（TIP）模型。在多人类-多机器人团队中，我们假设人类智能体对机器人存在两种体验类型：直接体验与间接体验。TIP模型提出了一种新颖的数学框架，明确纳入这两种体验类型。为评估该模型，我们开展了包含15对受试者（N=30）的人机实验，每对受试者使用两架无人机执行搜索与检测任务。结果表明，TIP模型成功捕捉了潜在信任动态，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-多机器人团队的计算信任建模数学框架。