Abstract
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.
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This project is funded by the U.S. Army Research Laboratory. Statements and opinions expressed do not necessarily reflect the position or the policy of the United States Government, and no official endorsement should be inferred.
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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: Ham, J., Karapanos, E., Morita, P., Burns, C. (eds) Persuasive Technology. PERSUASIVE 2018. Lecture Notes in Computer Science(), vol 10809. Springer, Cham. https://doi.org/10.1007/978-3-319-78978-1_5
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DOI: https://doi.org/10.1007/978-3-319-78978-1_5
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