Towards Automatic Learning of Discrete-Event Models from Simulations
Paper in proceedings, 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.


Ashfaq Hussain Farooqui

Chalmers, Electrical Engineering, Systems and control, Automation

Petter Falkman

Chalmers, Electrical Engineering, Systems and control, Automation

Martin Fabian

Chalmers, Electrical Engineering, Systems and control, Automation

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

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

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

Systematic testing of cyber-physical systems (SyTeC)

Swedish Research Council (VR), 2017-01-01 -- 2022-12-31.

Subject Categories

Other Computer and Information Science

Embedded Systems

Control Engineering



More information

Latest update