This dissertation addresses a key limitation of current industrial automation systems: their inability to flexibly interpret, plan, and execute variable ...

In this thesis, we develop data-driven methods for learning interpretable models of discrete and hybrid Cyber-Physical Systems (CPS). Manual modeling of complex CPS is often infeasible and prone to errors, ...