Model Checking as Control: Feedback Control for Statistical Model Checking of Cyber-Physical Systems

We introduce feedback-control statistical system checking (FC-SSC), a new approach to statistical model checking that exploits princi-ples of feedback-control for the analysis of cyber-physical systems (CPS). FC-SSC uses stochastic system identification to learn a CPS model, im-portance sampling to estimate the CPS state, and importance splitting to control the CPS so that the probability that the CPS satisfies a given property can be efficiently inferred. We illustrate the utility of FC-SSC on two example applications, each of which is simple enough to be easily understood, yet complex enough to exhibit all of FC-SCC's features. To the best of our knowledge, FC-SSC is the first statistical system checker to efficiently estimate the probability of rare events in realistic CPS ap-plications or in any complex probabilistic program whose model is either not available, or is infeasible to derive through static-analysis techniques