Trust is critical to the success of human-robot interaction. Research has shown that people will more accurately trust a robot if they have an accurate understanding of its decision-making process. The Partially Observable Markov Decision Process (POMDP) is one such decision-making process, but its quantitative reasoning is typically opaque to people. This lack of transparency is exacerbated when a robot can learn, making its decision making better, but also less predictable. Recent research has shown promise in calibrating human-robot trust by automatically generating explanations of POMDP-based decisions. In this work, we explore factors that can potentially interact with such explanations in influencing human decision-making in human-robot teams. We focus on explanations with quantitative expressions of uncertainty and experiment with common design factors of a robot: its embodiment and its communication strategy in case of an error. Results help us identify valuable properties and dynamics of the human-robot trust relationship.
Wang, N., Pynadath, D. V., Rovira, E., Barnes, M. J., & Hill, S. G. (2018). Is it my looks? Or something i said? The impact of explanations, embodiment, and expectations on trust and performance in human-robot teams. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10809 LNCS, pp. 56–69). Springer Verlag. https://doi.org/10.1007/978-3-319-78978-1_5