Towards Automatic Learning of Discrete-Event Models from Simulations
Paper i proceeding, 2018

Model-based techniques are, these days, being embraced by the manufacturing industry in their development frameworks. While model-based approaches allow for offline verification and validation before physical commissioning, and have other advantages over existing methods, they do have their own challenges. Firstly, models are typically created manually and hence are prone to errors. Secondly, once a model is created, tested, and put into use on the factory floor, there is an added effort required to maintain and update it. This paper is a preliminary study of the feasibility of automatically obtaining formal models from virtual simulations. We apply the foundational algorithm from the active automata learning community to study the requirements and enhancements needed to be able to derive discrete event models from virtual simulations. An abstract model in the form of operations is learned by applying this algorithm on a simulation model composed of discrete operations. While a major bottleneck to be solved is the generation of counterexamples, the results seem promising to apply model learning in practice.

Författare

Ashfaq Hussain Farooqui

Chalmers, Elektroteknik, System- och reglerteknik, Automation

Petter Falkman

Chalmers, Elektroteknik, System- och reglerteknik, Automation

Martin Fabian

Chalmers, Elektroteknik, System- och reglerteknik, Automation

14th IEEE International Conference on Automation Science and Engineering
Munich, Germany,

Systematisk testning av cyberfysiska system (SyTeC)

Vetenskapsrådet (VR), 2017-01-01 -- 2022-12-31.

Engineering Tool Chain for Efficient and Iterative Development of Smart Factories (ENTOC)

VINNOVA, 2016-09-01 -- 2019-08-31.

Ämneskategorier

Annan data- och informationsvetenskap

Inbäddad systemteknik

Reglerteknik

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2018-09-10