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.Comment: In Proceedings of Robotics: Science and Systems, 2023, Daegu, Korea.
arXiv admin note: text overlap with arXiv:2301.1092