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. We assert that in a multi-human multi-robot team, there exist two types of experiences that any human agent has with any 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：摘要：信任已被视为实现人-机器人有效协作的关键因素。现有信任建模研究主要聚焦于单人类智能体与单机器人交互的二元人-自主团队，而在由多个人类智能体和多个机器人智能体组成的团队中进行信任建模的研究极为匮乏。为填补这一研究空白，我们提出了面向多人类-多机器人团队的信任推理与传播模型（trust inference and propagation, TIP）。我们指出，在多人类-多机器人团队中，任何人类智能体与任何机器人之间存在两种经验类型：直接经验与间接经验。TIP模型提出了一种新颖的数学框架，明确纳入了这两种经验类型。为评估该模型，我们开展了一项包含15对受试者（N=30）的人因实验。每对受试者需与两架无人机共同完成搜索与检测任务。结果表明，我们的TIP模型成功捕捉了潜在的信任动态，并显著优于基准模型。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。