83 citations · 180 across the 12 of their papers we have counts for
12 papers
A Novel Spatial-Temporal Specification-Based Monitoring System for Smart Cities
Meiyi Ma, Ezio Bartocci, Eli Lifland +2
With the development of the Internet of Things, millions of sensors are being deployed in cities to collect real-time data. This leads to a need for checking city states against ci…
Safe Multi-Agent Reinforcement Learning via Shielding
Ingy Elsayed-Aly, Suda Bharadwaj, Christopher Amato +3
Multi-agent reinforcement learning (MARL) has been increasingly used in a wide range of safety-critical applications, which require guaranteed safety (e.g., no unsafe states are ev…
DeepTake: Prediction of Driver Takeover Behavior using Multimodal Data
Erfan Pakdamanian, Shili Sheng, Sonia Baee +3
Automated vehicles promise a future where drivers can engage in non-driving tasks without hands on the steering wheels for a prolonged period. Nevertheless, automated vehicles may…
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…
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…
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…
MEDIRL: Predicting the Visual Attention of Drivers via Maximum Entropy Deep Inverse Reinforcement Learning
Sonia Baee, Erfan Pakdamanian, Inki Kim +3
Inspired by human visual attention, we propose a novel inverse reinforcement learning formulation using Maximum Entropy Deep Inverse Reinforcement Learning (MEDIRL) for predicting…
SaSTL: Spatial Aggregation Signal Temporal Logic for Runtime Monitoring in Smart Cities
Meiyi Ma, Ezio Bartocci, Eli Lifland +2
We present SaSTL -- a novel Spatial Aggregation Signal Temporal Logic -- for the efficient runtime monitoring of safety and performance requirements in smart cities. We first descr…
A Logic-Based Learning Approach to Explore Diabetes Patient Behaviors
Josephine Lamp, Simone Silvetti, Marc Breton +2
Type I Diabetes (T1D) is a chronic disease in which the body's ability to synthesize insulin is destroyed. It can be difficult for patients to manage their T1D, as they must contro…
A Case Study of Trust on Autonomous Driving
Shili Sheng, Erfan Pakdamanian, Kyungtae Han +4
As autonomous vehicles have benefited the society, understanding the dynamic change of humans' trust during human-autonomous vehicle interaction can help to improve the safety and…
The Effect of Whole-Body Haptic Feedback on Driver's Perception in Negotiating a Curve
Erfan Pakdamanian, Lu Feng, Inki Kim
It remains uncertain regarding the safety of driving in autonomous vehicles that, after a long, passive control and inattention to the driving situation, how the drivers will be ef…
Counterexamples for Robotic Planning Explained in Structured Language
Lu Feng, Mahsa Ghasemi, Kai-Wei Chang +1
Automated techniques such as model checking have been used to verify models of robotic mission plans based on Markov decision processes (MDPs) and generate counterexamples that may…