Skip to main content
SpringerLink
Log in

Learning Process Steps as Dynamical Systems for a Sub-Symbolic Approach of Process Planning in Cyber-Physical Production Systems

  • Conference paper
  • First Online:
Artificial Intelligence. ECAI 2023 International Workshops (ECAI 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1948))

Included in the following conference series:

  • 227 Accesses

Abstract

Approaches in AI planning for Cyber-Physical Production Systems (CPPS) are mainly symbolic and depend on comprehensive formalizations of system domains and planning problems. Handcrafting such formalizations requires detailed knowledge of the formalization language, of the CPPS, and is overall considered difficult, tedious, and error-prone. Within this paper, we suggest a sub-symbolic approach for solving planning problems in CPPS. Our approach relies on neural networks that learn the dynamical behavior of individual process steps from global time-series observations of the CPPS and are embedded in a superordinate network architecture. In this context, we present the process step representation network architecture (peppr), a novel neural network architecture, which can learn the behavior of individual or multiple dynamical systems from global time-series observations. We evaluate peppr on real datasets from physical and biochemical CPPS, as well as artificial datasets from electrical and mathematical domains. Our model outperforms baseline models like multilayer perceptrons and variational autoencoders and can be considered as a first step towards a sub-symbolic approach for planning in CPPS.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    https://github.com/j-ehrhardt/peppr.

  2. 2.

    https://github.com/j-ehrhardt/ode-ml-datasets.

References

  1. Amado, L., Pereira, R.F., Meneguzzi, F.: Robust neuro-symbolic goal and plan recognition. Proc. AAAI Conf. Artif. Intell. 37(10), 11937–11944 (2023)

    Google Scholar 

  2. Ardizzone, L., Kruse, J., Rother, C., Köthe, U.: Analyzing inverse problems with invertible neural networks. In: International Conference on Learning Representations (2019)

    Google Scholar 

  3. Asai, M., Muise, C.: Learning neural-symbolic descriptive planning models via cube-space priors: the voyage home (to strips) (2020)

    Google Scholar 

  4. Balzereit, K., Niggemann, O.: Autoconf a new algorithm for reconfiguration of cyber-physical production systems. IEEE Trans. Industr. Inf. 19(1), 739–749 (2023)

    Article  Google Scholar 

  5. Bit-Monnot, A., Leofante, F., Pulina, L., Tacchella, A.: SMT-based planning for robots in smart factories. In: Wotawa, F., Friedrich, G., Pill, I., Koitz-Hristov, R., Ali, M. (eds.) IEA/AIE 2019. LNCS (LNAI), vol. 11606, pp. 674–686. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-22999-3_58

    Chapter  Google Scholar 

  6. Bunte, A., Stein, B., Niggemann, O.: Model-based diagnosis for cyber-physical production systems based on machine learning and residual-based diagnosis models. Proc. AAAI Conf. Artif. Intell. 33(01), 2727–2735 (2019)

    Google Scholar 

  7. Cashmore, M., Fox, M., Long, D., Magazzeni, D.: A compilation of the full PDDL+ language into SMT. In: Workshops at the Thirtieth AAAI Conference on Artificial Intelligence (2016)

    Google Scholar 

  8. Ehrhardt, J., Ramonat, M., Heesch, R., Balzereit, K., Diedrich, A., Niggemann, O.: An AI benchmark for diagnosis, reconfiguration & planning. In: 2022 IEEE 27th International Conference on Emerging Technologies and Factory Automation (ETFA), pp. 1–8. IEEE (2022)

    Google Scholar 

  9. ElMaraghy, H.A.: Changing and evolving products and systems – models and enablers. In: Springer Series in Advanced Manufacturing, pp. 25–45. Springer, London (2009). https://doi.org/10.1007/978-1-84882-067-8_2

  10. Ferber, P., Helmert, M., Hoffmann, J.: Neural network heuristics for classical planning: a study of hyperparameter space. In: European Conference on Artificial Intelligence (2020)

    Google Scholar 

  11. Ghallab, M., et al.: PDDL - the planning domain definition language. Technical Report CVC TR-98-003/DCS TR-1165 (1998)

    Google Scholar 

  12. Goldrick, S., Duran-Villalobos, C.A., Jankauskas, K., Lovett, D., Farid, S.S., Lennox, B.: Modern day monitoring and control challenges outlined on an industrial-scale benchmark fermentation process. Comput. Chem. Eng. 130, 106471 (2019)

    Article  Google Scholar 

  13. Grand, M., Pellier, D., Fiorino, H.: TempAMLSI: temporal action model learning based on STRIPS translation. In: Proceedings of the International Conference on Automated Planning and Scheduling, vol. 32, 597–605 (2022)

    Google Scholar 

  14. Hartung, F., et al.: Deep anomaly detection on tennessee eastman process data (2023)

    Google Scholar 

  15. Hoffmann, J., Nebel, B.: The FF planning system: fast plan generation through heuristic search. J. Artif. Intell. Res. 14, 253–302 (2001)

    Article  Google Scholar 

  16. Kagermann, H., Helbig, J., Hellinger, A., Wahlster, W.: Recommendations for implementing the strategic initiative INDUSTRIE 4.0: Securing the future of German manufacturing industry; final report of the Industrie 4.0 Working Group. Forschungsunion (2013)

    Google Scholar 

  17. Köcher, A., et al.: A research agenda for AI planning in the field of flexible production systems. In: 2022 IEEE 5th International Conference on Industrial Cyber-Physical Systems (ICPS), pp. 1–8 (2022)

    Google Scholar 

  18. Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. In: Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc. (2017)

    Google Scholar 

  19. Milani, A., Niyogi, R., Biondi, G.: Neural network based approach for learning planning action models. In: Misra, S., et al. (eds.) ICCSA 2019. LNCS, vol. 11624, pp. 526–537. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-24311-1_38

    Chapter  Google Scholar 

  20. Monostori, L.: Cyber-physical production systems: roots, expectations and r &d challenges. Procedia CIRP 17, 9–13 (2014)

    Article  Google Scholar 

  21. Müller, T., Jazdi, N., Schmidt, J.P., Weyrich, M.: Cyber-physical production systems: enhancement with a self-organized reconfiguration management. Procedia CIRP 99, 549–554 (2021)

    Article  Google Scholar 

  22. Multaheb, S., Bauer, F., Bretschneider, P., Niggemann, O.: Learning physically meaningful representations of energy systems with variational autoencoders. In: 2022 IEEE 27th International Conference on Emerging Technologies and Factory Automation (ETFA), pp. 1–6 (2022)

    Google Scholar 

  23. Nautrup, H.P., et al.: Operationally meaningful representations of physical systems in neural networks. Mach. Learn.: Sci. Technol. (2022)

    Google Scholar 

  24. Niggemann, O., Frey, C.: Data-driven anomaly detection in cyber-physical production systems. At - Automatisierungstechnik 63(10), 821–832 (2015)

    Article  Google Scholar 

  25. van den Oord, A., et al.: WaveNet: a generative model for raw audio. arXiv preprint arXiv:1609.03499 (2016)

  26. Toyer, S., Thiébaux, S., Trevizan, F., Xie, L.: ASNets: deep learning for generalised planning. J. Artif. Intell. Res. 68, 1–68 (2020)

    Article  MathSciNet  Google Scholar 

  27. Zhang, Y., Lee, K., Lee, H.: Augmenting supervised neural networks with unsupervised objectives for large-scale image classification. In: Proceedings of the 33rd International Conference on International Conference on Machine Learning, vol. 48, pp. 612–621. ICML’16, JMLR.org (2016)

    Google Scholar 

Download references

Acknowledgements

This research as part of the projects LaiLa and EKI is funded by dtec.bw - Digitalization and Technology Research Center of the Bundeswehr which we gratefully acknowledge. dtec.bw is funded by the European Union - NextGenerationEU.

Author information

Authors and Affiliations

  1. Helmut-Schmidt-University, Hamburg, Germany

    Jonas Ehrhardt, René Heesch & Oliver Niggemann

Authors
  1. Jonas Ehrhardt
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. René Heesch
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Oliver Niggemann
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jonas Ehrhardt .

Editor information

Editors and Affiliations

  1. Halmstad University, Halmstad, Sweden

    Sławomir Nowaczyk

  2. Warsaw University of Technology, Warsaw, Poland

    Przemysław Biecek

  3. Warsaw University, Warsaw, Poland

    Neo Christopher Chung

  4. University of Huddersfield, Huddersfield, UK

    Mauro Vallati

  5. AGH University of Science and Technology, Kraków, Poland

    Paweł Skruch

  6. AGH University of Science and Technology, Kraków, Poland

    Joanna Jaworek-Korjakowska

  7. University of Huddersfield, Huddersfield, UK

    Simon Parkinson

  8. University of Huddersfield, Huddersfield, UK

    Alexandros Nikitas

  9. Universität Osnabrück, Osnabrück, Germany

    Martin Atzmüller

  10. University of Economics Prague, Prague, Czech Republic

    Tomáš Kliegr

  11. University of Bamberg, Bamberg, Germany

    Ute Schmid

  12. Jagiellonian University, Kraków, Poland

    Szymon Bobek

  13. Jožef Stefan Institute, Ljubljana, Slovenia

    Nada Lavrac

  14. HU University of Applied Sciences Utrecht, Utrecht, The Netherlands

    Marieke Peeters

  15. Rotterdam University of Applied Sciences, Rotterdam, The Netherlands

    Roland van Dierendonck

  16. Amsterdam University of Applied Sciences, Amsterdam, The Netherlands

    Saskia Robben

  17. University of Reims Champagne-Ardenne, Reims, France

    Eunika Mercier-Laurent

  18. Istanbul Technical University, Istanbul, Türkiye

    Gülgün Kayakutlu

  19. Wroclaw University of Economics and Business, Wrocław, Poland

    Mieczyslaw Lech Owoc

  20. University of Galway, Galway, Ireland

    Karl Mason

  21. University of Galway, Galway, Ireland

    Abdul Wahid

  22. University of Calabria, Rende, Italy

    Pierangela Bruno

  23. University of Calabria, Rende, Italy

    Francesco Calimeri

  24. Marche Polytechnic University, Ancona, Italy

    Francesco Cauteruccio

  25. University of Calabria, Rende, Italy

    Giorgio Terracina

  26. University of Bamberg, Bamberg, Germany

    Diedrich Wolter

  27. Coburg University of Applied Sciences, Coburg, Germany

    Jochen L. Leidner

  28. FAU Erlangen-Nürnberg, Erlangen, Germany

    Michael Kohlhase

  29. University of Leeds, Leeds, UK

    Vania Dimitrova

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Ehrhardt, J., Heesch, R., Niggemann, O. (2024). Learning Process Steps as Dynamical Systems for a Sub-Symbolic Approach of Process Planning in Cyber-Physical Production Systems. In: Nowaczyk, S., et al. Artificial Intelligence. ECAI 2023 International Workshops. ECAI 2023. Communications in Computer and Information Science, vol 1948. Springer, Cham. https://doi.org/10.1007/978-3-031-50485-3_34

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-50485-3_34

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-50484-6

  • Online ISBN: 978-3-031-50485-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation