Models of actual causality leverage domain knowledge to generate convincing diagnoses of
events that caused an outcome. It is promising to apply these models to diagnose and repair …

Learning-enabled controllers have been adopted in various cyber-physical systems (CPS).
When a learning-enabled controller fails to accomplish its task from a set of initial states …

Complex relations between cybernetic and physical components of a cyber-physical system
(CPS) in tandem with continuous environment changes represent a challenge to …

State-of-the-art machine-learned controllers for autonomous systems demonstrate
unbeatable performance in scenarios known from training. However, in evolving …

Learning Enabled Components (LEC) have greatly assisted cyber-physical systems in
achieving higher levels of autonomy. However, LEC's susceptibility to dynamic and …

Cyberphysical systems (CPSs) are vulnerable to catastrophic fault propagation due to the
strong connectivity among their subsystems. This article introduces a learning-based …

Modern cyber-physical systems (CPS) integrate machine learning and deep learning
components for a variety of tasks, including sensing, control, anomaly detection and learning …

Increasingly, safety-critical systems include artificial intelligence and machine learning
components (ie, Learning-Enabled Components (LECs)). However, when behavior is …

An essential aspect of resilience within Cyber-Physical Systems stands in their capacity of
early detection of faults before they generate failures. Faults can be of any origin, either …

With the increasing complexity of Cyber-Physical Systems, their behavior and decisions
become increasingly difficult to understand and comprehend for users and other …