Visible to the public Would you Take Advice from a Robot? Developing a Framework for Inferring Human-Robot Trust in Time-Sensitive Scenarios

TitleWould you Take Advice from a Robot? Developing a Framework for Inferring Human-Robot Trust in Time-Sensitive Scenarios
Publication TypeConference Paper
Year of Publication2020
AuthorsXu, J., Howard, A.
Conference Name2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)
Date PublishedAug. 4
ISBN Number978-1-7281-6075-7
Keywordsdecision making, experimental scenarios, generalized model, Hidden Markov models, Human Behavior, human decision-making process, human factors, human subject studies, human-robot interaction, human-robot trust, interactive scenarios, model trust, pubcrawl, regression analysis, resilience, Resiliency, Robot Trust, successful human-robot interaction, time-sensitive scenarios, trust states

Trust is a key element for successful human-robot interaction. One challenging problem in this domain is the issue of how to construct a formulation that optimally models this trust phenomenon. This paper presents a framework for modeling human-robot trust based on representing the human decision-making process as a formulation based on trust states. Using this formulation, we then discuss a generalized model of human-robot trust based on Hidden Markov Models and Logistic Regression. The proposed approach is validated on datasets collected from two different human subject studies in which the human is provided the ability to take advice from a robot. Both experimental scenarios were time-sensitive, in that a decision had to be made by the human in a limited time period, but each scenario featured different levels of cognitive load. The experimental results demonstrate that the proposed formulation can be utilized to model trust, in which the system can predict whether the human will decide to take advice (or not) from the robot. It was found that our prediction performance degrades after the robot made a mistake. The validation of this approach on two scenarios implies that this model can be applied to other interactive scenarios as long as the interaction dynamics fits into the proposed formulation. Directions for future improvements are discussed.

Citation Keyxu_would_2020