  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.
