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This paper addresses the problem of data-driven model discrimination forunknown switched systems with unknown linear temporal logic (LTL)specifications, representing tasks, that govern their mode sequences, whereonly sampled data of the unknown dynamics and tasks are available. To tacklethis problem, we propose data-driven methods to over-approximate the unknowndynamics and to infer the unknown specifications such that both set-membershipmodels of the unknown dynamics and LTL formulas are guaranteed to include theground truth model and specification/task. Moreover, we present anoptimization-based algorithm for analyzing the distinguishability of a set oflearned/inferred model-task pairs as well as a model discrimination algorithmfor ruling out model-task pairs from this set that are inconsistent with newobservations at run time. Further, we present an approach for reducing the sizeof inferred specifications to increase the computational efficiency of themodel discrimination algorithms.

Data-Driven Model Discrimination of Switched Nonlinear Systems with Temporal Logic Inference