Reinforcement Learning (RL) has achieved tremendous success in many complex decision-
making tasks. However, safety concerns are raised during deploying RL in real-world …

Deep reinforcement learning (DRL) has proven capable of superhuman performance on
many complex tasks. To achieve this success, DRL algorithms train a decision-making agent …

Swarm Intelligence (SI) is a popular multi-agent framework that has been originally inspired
by swarm behaviors observed in natural systems, such as ant and bee colonies. In a system …

Autonomous cyber-physical systems (CPS) can improve safety and efficiency for safety-
critical applications, but require rigorous testing before deployment. The complexity of these …

The last decade witnessed an ever-increasing stream of successes in Machine Learning
(ML). These successes offer clear evidence that ML is bound to become pervasive in a wide …

Deep learning methods can be used to produce control policies, but certifying their safety is
challenging. The resulting networks are nonlinear and often very large. In response to this …

Formal methods are mathematically based techniques for the rigorous development of
software-intensive systems. The railway signaling domain is a field in which formal methods …

The increasing complexity and connectivity of automotive systems have raised concerns
about their vulnerability to security breaches. As a result, the integration of formal methods …

There has so far been no shared understanding of validity in agent-based simulation. We
here conceptualise validation as systematically substantiating the premises on which …

Deep neural networks (DNNs) have gained significant popularity in recent years, becoming
the state of the art in a variety of domains. In particular, deep reinforcement learning (DRL) …