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Towards Engineering Digital Twins by Active Behaviour Mining

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Model Checking, Synthesis, and Learning

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 13030))

Abstract

In the context of Confirm, the Irish Research Centre on Smart Manufacturing, field demonstrators are used to show new techniques to industrial partners, various kinds of students, and the general public alike. Considering the robotics demonstrator for the Digital Thread concept used in Confirm, which is a small cyberphysical system based on the UR3 cobot and a web controller for it, we apply Active Automata Learning in order to obtain a Digital Twin for it. Behavior mining done in this fashion is nowadays uncommon, but it has various advantages over, e.g., models obtained with popular AI techniques in that the AAL models are accurate deterministic behavioural explanations for the system behaviour at the chosen level of abstraction, and they may be further amenable to formal verification, e.g., by model checking, in order to establish properties of interest.

This extension has the effect of showcasing the Digital Twin concept, the AAL technique, the use of model checking, and the importance of working with formal models that are amenable to these technologies. We then reflect on the nature of the models and their uses and meaning, from the point of view of the comments and questions we receive in the demonstrations. We also consider the use of a feature-based approach to modelling the systems and their interactions, which is a further aspect for which the demonstrator could be used, with a special attention to the aspects of this work, like AAL and the feature based and feature interaction research, that connect directly with the collaboration with and the research of Bengt Jonsson.

This work was supported, in part, by Science Foundation Ireland grant 16/RC/3918 to Confirm, the Smart Manufacturing SFI Research Centre (www.confirm.ie) and 13/RC/2094 to Lero - The SFI Research Centre for Software (www.lero.ie).

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Notes

  1. 1.

    https://youtu.be/RaOejcczPas.

  2. 2.

    https://learnlib.de.

  3. 3.

    https://github.com/learnlib/learnlib-studio.

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Authors and Affiliations

  1. Department of Computer Science and Information Systems, University of Limerick, Limerick, Ireland

    Tiziana Margaria & Alexander Schieweck

  2. Lero - The SFI Research Centre for Software, Limerick, Ireland

    Tiziana Margaria & Alexander Schieweck

  3. Confirm - Smart Manufacturing SFI Research Centre, Limerick, Ireland

    Tiziana Margaria & Alexander Schieweck

Authors
  1. Tiziana Margaria
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  2. Alexander Schieweck
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Correspondence to Tiziana Margaria or Alexander Schieweck .

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Editors and Affiliations

  1. Universität Oldenburg, Oldenburg, Germany

    Ernst-Rüdiger Olderog

  2. TU Dortmund University, Dortmund, Germany

    Bernhard Steffen

  3. Uppsala University, Uppsala, Sweden

    Wang Yi

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Margaria, T., Schieweck, A. (2021). Towards Engineering Digital Twins by Active Behaviour Mining. In: Olderog, ER., Steffen, B., Yi, W. (eds) Model Checking, Synthesis, and Learning. Lecture Notes in Computer Science(), vol 13030. Springer, Cham. https://doi.org/10.1007/978-3-030-91384-7_8

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