Towards Explainable Process Predictions for Industry 4.0 in the DFKI-Smart-Lego-Factory
Zusammenfassung
With the advent of digitization on the shopfloor and the developments of Industry 4.0, companies are faced with opportunities and challenges alike. This can be illustrated by the example of AI-based process predictions, which can be valuable for real-time process management in a smart factory. However, to constructively collaborate with such a prediction, users need to establish confidence in its decisions. Explainable artificial intelligence (XAI) has emerged as a new research area to enable humans to understand, trust, and manage the AI they work with. In this contribution, we illustrate the opportunities and challenges of process predictions and XAI for Industry 4.0 with the DFKI-Smart-Lego-Factory. This fully automated factory prototype built out of LEGO $$^\circledR$$ ® bricks demonstrates the potentials of Industry 4.0 in an innovative, yet easily accessible way. It includes a showcase that predicts likely process outcomes and uses state-of-the-art XAI techniques to explain them to its workers and visitors.
- Vollständige Referenz
- BibTeX
Rehse, J.-R., Mehdiyev, N. & Fettke, P.,
(2019).
Towards Explainable Process Predictions for Industry 4.0 in the DFKI-Smart-Lego-Factory.
KI - Künstliche Intelligenz: Vol. 33, No. 2.
Springer.
(S. 181-187).
DOI: 10.1007/s13218-019-00586-1
@article{mci/Rehse2019,
author = {Rehse, Jana-Rebecca AND Mehdiyev, Nijat AND Fettke, Peter},
title = {Towards Explainable Process Predictions for Industry 4.0 in the DFKI-Smart-Lego-Factory},
journal = {KI - Künstliche Intelligenz},
volume = {33},
number = {2},
year = {2019},
,
pages = { 181-187 } ,
doi = { 10.1007/s13218-019-00586-1 }
}
author = {Rehse, Jana-Rebecca AND Mehdiyev, Nijat AND Fettke, Peter},
title = {Towards Explainable Process Predictions for Industry 4.0 in the DFKI-Smart-Lego-Factory},
journal = {KI - Künstliche Intelligenz},
volume = {33},
number = {2},
year = {2019},
,
pages = { 181-187 } ,
doi = { 10.1007/s13218-019-00586-1 }
}
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Mehr Information
ISSN: 1610-1987
Datum: 2019
Typ: Text/Journal Article

