Learning Models of Cyber-Physical Systems using Automata Learning

In this paper we examine two case studies in which we learn finite state machines from models of CPS using automata learning. We explore how well automata learning is suited as an approach for learning CPS. What challenges and problems exist when trying to learn a model of a CPS using automata learning. Automata learning can reliably learn finite state machines of systems like embedded systems or software systems. CPS pose different challenges, like continuous components, for which different levels of abstractions and considerations have to be used, so the resulting finite state machines are useful representations of the systems. Through the small, yet insightful case studies we show examples of how automata learning can be applied to CPS and what information the resulting automata can represent.