1 citations · 2 across the 2 of their papers we have counts for
3 papers
cs.AI2022★ 1 cited
Toward Policy Explanations for Multi-Agent Reinforcement Learning
Kayla Boggess, Sarit Kraus, Lu Feng
Advances in multi-agent reinforcement learning (MARL) enable sequential decision making for a range of exciting multi-agent applications such as cooperative AI and autonomous drivi…
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.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…