2021/06/03 by Hebert Azevedo-Sa, Azevedo-Sa, Hebert, X. Jessie Yang +6 · 1 citation
Computer Science · Psychology · Social Sciences · #Access Control and Trust #Cognitive Functions and Memory #FOS: Computer and information sciences #Human-Automation Interaction and Safety #Robotics (cs.RO) #cs.RO
paper · pdf · doi:10.48550/arxiv.2106.02194
8 pages, 5 figures, conditionally accepted for publication on IEEE Robotics and Automation Letters
openalex publication_date 2021/06/03 · arxiv created 2021/06/04 · arxiv updated 2021/06/07 · openalex created_date 2022/07/25 · openalex updated_date 2026/07/28
We introduce a novel capabilities-based bi-directional multi-task trust model that can be used for trust prediction from either a human or a robotic trustor agent. Tasks are represented in terms of their capability requirements, while trustee agents are characterized by their individual capabilities. Trustee agents' capabilities are not deterministic; they are represented by belief distributions. For each task to be executed, a higher level of trust is assigned to trustee agents who have demonstrated that their capabilities exceed the task's requirements. We report results of an online experiment with 284 participants, revealing that our model outperforms existing models for multi-task trust prediction from a human trustor. We also present simulations of the model for determining trust from a robotic trustor. Our model is useful for control authority allocation applications that involve human-robot teams.