Analyzing Cyber-Physical Systems from the Perspective of Artificial Intelligence
It tackles uncertainty in CPS analysis for researchers and engineers, but appears incremental as it focuses on comparing existing perspectives.
This paper compares traditional methods for analyzing cyber-physical systems with AI-based approaches, particularly reinforcement learning, to address uncertainty from factors like user behavior and unpredictable events.
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.