16 citations · 18 across the 5 of their papers we have counts for
5 papers
Explainable Multi-Agent Reinforcement Learning for Temporal Queries
Kayla Boggess, Sarit Kraus, Lu Feng
As multi-agent reinforcement learning (MARL) systems are increasingly deployed throughout society, it is imperative yet challenging for users to understand the emergent behaviors o…
Enjoy the Ride Consciously with CAWA: Context-Aware Advisory Warnings for Automated Driving
Erfan Pakdamanian, Erzhen Hu, Shili Sheng +3
In conditionally automated driving, drivers decoupled from driving while immersed in non-driving-related tasks (NDRTs) could potentially either miss the system-initiated takeover r…
A Study on Learning and Simulating Personalized Car-Following Driving Style
Shili Sheng, Erfan Pakdamanian, Kyungtae Han +2
Automated vehicles are gradually entering people's daily life to provide a comfortable driving experience for the users. The generic and user-agnostic automated vehicles have limit…
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…
Planning for Automated Vehicles with Human Trust
Shili Sheng, Erfan Pakdamanian, Kyungtae Han +4
Recent work has considered personalized route planning based on user profiles, but none of it accounts for human trust. We argue that human trust is an important factor to consider…