83 citations · 117 across the 6 of their papers we have counts for
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cs.RO2020★ 1 cited
Towards Personalized Explanation of Robot Path Planning via User Feedback
Kayla Boggess, Shenghui Chen, Lu Feng
Prior studies have found that explaining robot decisions and actions helps to increase system transparency, improve user understanding, and enable effective human-robot collaborati…
cs.LG2020
Predictive Monitoring with Logic-Calibrated Uncertainty for Cyber-Physical Systems
Meiyi Ma, John Stankovic, Ezio Bartocci +1
Predictive monitoring -- making predictions about future states and monitoring if the predicted states satisfy requirements -- offers a promising paradigm in supporting the decisio…
cs.RO2020
Towards Transparent Robotic Planning via Contrastive Explanations
Shenghui Chen, Kayla Boggess, Lu Feng
Providing explanations of chosen robotic actions can help to increase the transparency of robotic planning and improve users' trust. Social sciences suggest that the best explanati…