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Title: TIP: A Trust Inference and Propagation Model in Multi-Human Multi-Robot Teams
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.  more » « less
Award ID(s):
2045009
PAR ID:
10404091
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
HRI '23: Companion of the 2023 ACM/IEEE International Conference on Human-Robot Interaction
Page Range / eLocation ID:
639 to 643
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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