  A common technique to verify complex logic specifications for dynamical
systems is the construction of symbolic abstractions: simpler, finite-state
models whose behaviour mimics the one of the systems of interest. Typically,
abstractions are constructed exploiting an accurate knowledge of the underlying
model: in real-life applications, this may be a costly assumption. By sampling
random $\ell$-step trajectories of an unknown system, we build an abstraction
based on the notion of $\ell$-completeness. We newly define the notion of
probabilistic behavioural inclusion, and provide probably approximately correct
(PAC) guarantees that this abstraction includes all behaviours of the concrete
system, for finite and infinite time horizon, leveraging the scenario theory
for non convex problems. Our method is then tested on several numerical
benchmarks.
