  Principles of modern cyber-physical system (CPS) analysis are based on
analytical methods that depend on whether safety or liveness requirements are
considered. Complexity is abstracted through different techniques, ranging from
stochastic modelling to contracts. However, both distributed heuristics and
Artificial Intelligence (AI)-based approaches as well as the user perspective
or unpredictable effects, such as accidents or the weather, introduce enough
uncertainty to warrant reinforcement-learning-based approaches. This paper
compares traditional approaches in the domain of CPS modelling and analysis
with the AI researcher perspective to exploring unknown complex systems.
