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.

翻译：本文针对未知切换系统在未知线性时序逻辑（LTL）规格（表示任务）控制其模式序列时的数据驱动模型辨识问题展开研究，其中仅能获取未知动力学与任务的采样数据。为解决该问题，我们提出数据驱动方法对未知动力学进行过逼近，并对未知规格进行推理，使得未知动力学的集员模型与LTL公式均能保证包含真实模型及规格/任务。此外，我们提出一种基于优化的算法分析一组学习/推理得到的模型-任务对的可区分性，以及一种在运行过程中剔除与新观测不一致的模型-任务对的模型辨识算法。进一步，我们提出一种缩减推理规格规模的方法，以提高模型辨识算法的计算效率。