Towards Automatic Learning of Discrete-Event Models from Simulations

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 chal...

Full description

Saved in:
Bibliographic Details
Main Authors: Farooqui, Ashfaq, Falkman, Petter, Fabian, Martin
Format: Conference Proceeding
Language:English
Subjects:
Learning automata
Manipulators
Manufacturing industries
Production facilities
Protocols
Software
Software algorithms
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:2161-8089
2161-8070
2161-8089
DOI:10.1109/COASE.2018.8560451