MIDES:一个通过主动学习进行主管综合的工具

Ashfaq Farooqui, Fredrik Hagebring, Martin Fabian
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引用次数: 2

摘要

提出了一种用于离散事件系统模型和监督器自动学习的工具MIDES。该工具与目标系统的模拟接口,通过交互学习行为模型。根据预期的结果,有几种不同的算法可供选择。此外,给定一组规范,该工具学习一个可以帮助确保受控系统保证规范的主管。此外,通过学习模块化的监督器来解决状态空间爆炸问题。在本文中,我们介绍了该工具,它的接口和算法。我们通过几个案例研究来证明它的有用性。
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MIDES: A Tool for Supervisor Synthesis via Active Learning
A tool, MIDES, for automatic learning of models and supervisors for discrete event systems is presented. The tool interfaces with a simulation of the target system to learn a behavioral model through interaction. There are several different algorithms to choose from depending on the intended outcome. Moreover, given a set of specifications, the tool learns a supervisor that can help ensure the controlled system guarantees the specifications. Furthermore, the state-space explosion problem is addressed by learning a modular supervisor. In this paper, we introduce the tool, its interfaces, and algorithms. We demonstrate the usefulness through several case studies.
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