The adoption of digital technology in industrial control systems (ICS) enables improved
control over operation, ease of system diagnostics and reduction in cost of maintenance of …

This paper reviews the reasons that Human-in-the-Loop is both critical for preventing widely-
understood failure modes for machine learning, and not a practical solution. Following this …

Operation and maintenance of large distributed cloud applications can quickly become
unmanageably complex, putting human operators under immense stress when problems …

Reinforcement Learning (RL) is an exciting field of machine learning offering many
applications in the fields of robotics, 1 wind energy conversion2 and Unmanned Aerial …

This thesis demonstrates that autonomous cyber-physical systems that use machine
learning for control are amenable to formal verification. Cyber-physical systems, such as …

Since deep learning systems do not generalize well when training data is incomplete and
missing coverage of corner cases, it is difficult to ensure the robustness of safety-critical self …

Model-based reinforcement learning has been widely studied for controller synthesis in
cyber-physical systems (CPSs). In particular, for safety-critical CPSs, it is important to …

Cyber-physical systems (CPSs) are ubiquitous ranging from smart household appliances to
drones and self-driving cars, and are becoming increasingly important in the functioning of …

Cyber-Physical Systems (CPSs) are composed of computational control logic and physical
processes, which intertwine with each other. CPSs are widely used in various domains of …

Formal verification provides a high degree of confidence in safe system operation, but only if
reality matches the verified model. Although a good model will be accurate most of the time …