Abstract Machine learning (ML) algorithms allow computers to learn without being explicitly
programmed. Their utilization is spreading to highly sophisticated tasks across multiple …

Abstract Machine Learning algorithms are becoming more prevalent in critical systems
where dynamic decision making and efficiency are the goal. As is the case for complex and …

Machine Learnt (ML) components are now widely accepted for use in a range of
applications with results that are reported to exceed, under certain conditions, human …

Machine learning (ML) applications generate a continuous stream of success stories from
various domains. ML enables many novel applications, also in safety-critical contexts …

Machine Learning (ML) is now used in a range of systems with results that are reported to
exceed, under certain conditions, human performance. Many of these systems, in domains …

There is increasing interest in the application of machine learning (ML) technologies to
safety-critical cyber-physical systems, with the promise of increased levels of autonomy due …

Machine Learning (ML) is increasingly applied for the control of safety-critical Cyber-
Physical Systems (CPS) in application areas that cannot easily be mastered with traditional …

Nowadays, Machine Learning (ML) algorithms are being incorporated into many systems
since they can learn and solve complex problems. Some of these systems can be …

The strong demand for more automated transport systems with enhanced safety, in
conjunction with the explosion of technologies and products implementing machine learning …

Reinforcement Learning (RL) algorithms are showing promising results in simulated
environments, but their replication in real physical applications, even more so in safety …