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- W3129573809 abstract "Trust is an important factor in the interaction between humans and automation that can mediate the reliance of human operators. In this work, we evaluate a computational model of human trust on swarm systems based on Sheridan (2019)’s modified Kalman estimation model using existing experiment data (Nam, Li, Li, Lewis, & Sycara, 2018). Results show that our Kalman Filter model outperforms existing state of the art alternatives including dynamic Bayesian networks and inverse reinforcement learning. This work is novel in that: 1) The Kalman estimator is the first computational model formulating the human trust evolution as a combination of both open-loop trust anticipation and closed-loop trust feedback. 2) The proposed model considers the operator’s cognitive time lag between perceiving and processing the system display. 3) The proposed model provides a personalized model for each individual and reaches a better level of fitness than state-of-the-art alternatives." @default.
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- W3129573809 date "2020-12-01" @default.
- W3129573809 modified "2023-09-27" @default.
- W3129573809 title "A Kalman estimation model of human trust in supervisory control of robotic swarms" @default.
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- W3129573809 doi "https://doi.org/10.1177/1071181320641075" @default.
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