2021/06/03 by Azevedo-Sa, Hebert, Yang, X. Jessie, Robert, Lionel + "Jr" +1
Social Sciences · #Artificial Trust #Bi-directional trust #Human Robot Collaboration #Human Robot Interaction #Human-Robot Collaboration #Human-Robot Interaction #Human-Robot Trust #Information Science #Natural Trust #Social Sciences #Teamwork trust #artificial intelligence trust #autonomous agent trust #bi-directional trust #capabilities based bidirectional multi-task trust model #capabilities based trust model #capabilities-based bidirectional trust model #capabilities-based trust model #computer supported collaborative work #human autonomous agent interaction #human computer interaction #human robot control authority allocation #human robot teaming #human robot teams #human robot trust model #human robot work #human–robot teams #multi-task trust model #multi-task trust prediction #robot control authority allocation #robot trust #robot trust prediction #robotic trustor #trust
paper · doi:10.7302/1286
We introduce a novel capabilities-based bidirectional 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.