Enabling Team of Teams: A Trust Inference and Propagation (TIP) Model in Multi-Human Multi-Robot Teams

TL;DR

This paper introduces the TIP model, a novel mathematical framework for trust inference and propagation in multi-human multi-robot teams, addressing a significant research gap in trust modeling beyond dyadic interactions.

Contribution

The paper presents the first computational trust model specifically designed for multi-human multi-robot teams, incorporating both direct and indirect trust experiences.

Findings

TIP model accurately captures trust dynamics

Significantly outperforms baseline models

Validated through human-subject experiment with drones

Abstract

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.