Trust has been identified as one central factor in effective human-autonomy interaction. In this talk, I will present and synthesize the results of two studies examining trust dynamics in human-autonomy interaction. In study 1, we identify and define three properties of trust dynamics, namely continuity, negativity bias, and stabilization. The three properties characterize a human agent's trust formation and evolution process de facto. In study 2, we propose a computational model of trust dynamics that adheres to the three properties and evaluate the computational model against existing trust inference models. Results show that our model provides superior performance.
Dr. Jessie Yang is an Assistant Professor in the Department of Industrial and Operations Engineering at the University of Michigan, with a courtesy appointment at the School of Information. She obtained her PhD and MEng in Mechanical & Aerospace Engineering (Human Factors), and her BEng in Electrical and Electronic Engineering all from Nanyang Technological University, Singapore. Prior to joining U-M, she worked as a postdoctoral fellow at MIT. Dr. Yang's research interests include human-autonomy/robot interaction, human factors in high-risk industries, and user experience design. Her research has been funded by NSF, ARL, AFOSR, AAA Foundation for Traffic Safety, as well as industrial partners.
Meeting ID: 984 8271 3919
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