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Learning an automaton that approximates the behavior of a black-box system isa long-studied problem. Besides its theoretical significance, its applicationto search-based testing and model understanding is recently recognized. Wepresent an algorithm to learn a nonlinear hybrid automaton (HA) thatapproximates a black-box hybrid system (HS) from a set of input--output tracesgenerated by the HS. Our method is novel in handling (1) both exogenous andendogenous HS and (2) HA with reset associated with each transition. To ourknowledge, ours is the first method that achieves both features. We applied ouralgorithm to various benchmarks and confirmed its effectiveness.

Learning nonlinear hybrid automata from input--output time-series data