MIDES: A Tool for Supervisor Synthesis via Active Learning
Paper i proceeding, 2021

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.

Författare

Ashfaq Hussain Farooqui

Chalmers, Elektroteknik, System- och reglerteknik, Automation

Fredrik Hagebring

Chalmers, Elektroteknik, System- och reglerteknik, Automation

Martin Fabian

Chalmers, Elektroteknik, System- och reglerteknik, Automation

IEEE International Conference on Automation Science and Engineering

21618070 (ISSN) 21618089 (eISSN)

Vol. 2021-August 792-797

17th IEEE International Conference on Automation Science and Engineering, CASE 2021
Lyon, France,

Ämneskategorier

Inbäddad systemteknik

Datavetenskap (datalogi)

Datorsystem

DOI

10.1109/CASE49439.2021.9551435

Mer information

Senast uppdaterat

2021-11-09