  This paper addresses the problem of data-driven model discrimination for
unknown switched systems with unknown linear temporal logic (LTL)
specifications, representing tasks, that govern their mode sequences, where
only sampled data of the unknown dynamics and tasks are available. To tackle
this problem, we propose data-driven methods to over-approximate the unknown
dynamics and to infer the unknown specifications such that both set-membership
models of the unknown dynamics and LTL formulas are guaranteed to include the
ground truth model and specification/task. Moreover, we present an
optimization-based algorithm for analyzing the distinguishability of a set of
learned/inferred model-task pairs as well as a model discrimination algorithm
for ruling out model-task pairs from this set that are inconsistent with new
observations at run time. Further, we present an approach for reducing the size
of inferred specifications to increase the computational efficiency of the
model discrimination algorithms.
