Machine learning models that use deep neural networks (DNNs) are vulnera...
Autonomous cyber and cyber-physical systems need to perform decision-mak...
The data used to train deep neural network (DNN) models in applications ...
Machine learning models in the wild have been shown to be vulnerable to
...
Machine learning (ML) models that use deep neural networks are vulnerabl...
Cyber and cyber-physical systems equipped with machine learning algorith...
Reinforcement learning involves agents interacting with an environment t...
This paper considers multi-agent reinforcement learning (MARL) tasks whe...
The resilience of cyberphysical systems to denial-of-service (DoS) and
i...
The inputs and preferences of human users are important considerations i...
Multi-agent reinforcement learning involves multiple agents interacting ...
This paper studies the control of safety-critical dynamical systems in t...
A cyber-physical system (CPS) is expected to be resilient to more than o...
This paper studies the synthesis of control policies for an agent that h...
Reinforcement learning has been successful in training autonomous agents...
This paper studies the satisfaction of a class of temporal properties fo...
This paper augments the reward received by a reinforcement learning agen...
This paper studies the synthesis of control policies for an agent that h...
We formulate notions of opacity for cyberphysical systems modeled as
dis...