A Unified Bi-Directional Model for Natural and Artificial Trust in Human-Robot Collaboration
A Unified Bi-Directional Model for Natural and Artificial Trust in Human-Robot Collaboration
复制标题
人-机器人协作中自然和人工信任的统一双向模型
DOI：
10.1109/lra.2021.3088082
复制
发表时间：
2021-07-01
影响因子：
5.2
通讯作者：
Tilbury, Dawn M.
中科院分区：
文献类型：
--
作者：
Azevedo-Sa, Hebert;Yang, X. Jessie;Tilbury, Dawn M.
我们提出了一种新的基于能力的双向多任务信任模型，该模型可以用于人类或机器人信任代理的信任预测。任务用它们的能力需求来表示，而受托代理则用它们各自的能力来表征。受托代理的能力不是确定性的；它们由信念分布表示。对于要执行的每个任务，将更高级别的信任分配给已证明其能力超出任务要求的受托代理。我们报告了一个有284人参与的在线实验的结果，结果表明，我们的模型比现有的人类信任者的多任务信任预测模型性能更好。我们还给出了用于确定机器人信任者的信任的模型的模拟。我们的模型对于涉及人类-机器人团队的控制权分配应用是有用的。
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.