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Summary of "A Lazy Approach to Neural Numerical Planning with Control Parameters" (Extended Abstract)

Authors René Heesch , Alessandro Cimatti , Jonas Ehrhardt , Alexander Diedrich , Oliver Niggemann

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Subject Classification

ACM Subject Classification
  • Computing methodologies → Planning with abstraction and generalization
Keywords
  • Satisfiability Modulo Theory
  • Neural Numerical Planning with Control Parameters
  • Neural Networks

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Abstract

This is an extended abstract of the manuscript "A Lazy Approach to Neural Numerical Planning with Control Parameters" [René Heesch et al., 2024]. The paper presents a lazy, hierarchical approach to tackle the challenge of planning in complex numerical domains, where the effects of actions are influenced by control parameters, and may be described by neural networks.

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René Heesch, Alessandro Cimatti, Jonas Ehrhardt, Alexander Diedrich, and Oliver Niggemann. Summary of "A Lazy Approach to Neural Numerical Planning with Control Parameters" (Extended Abstract). In 35th International Conference on Principles of Diagnosis and Resilient Systems (DX 2024). Open Access Series in Informatics (OASIcs), Volume 125, pp. 32:1-32:3, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024) https://doi.org/10.4230/OASIcs.DX.2024.32

Author Details

René Heesch
  • Helmut-Schmidt-University, Hamburg, Germany
Alessandro Cimatti
  • Fondazione Bruno Kessler, Trento, Italy
Jonas Ehrhardt
  • Helmut-Schmidt-University, Hamburg, Germany
Alexander Diedrich
  • Helmut-Schmidt-University, Hamburg, Germany
Oliver Niggemann
  • Helmut-Schmidt-University, Hamburg, Germany

Funding

This research as part of the projects EKI and LaiLa 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.

Acknowledgements

This work has benefitted from Dagstuhl Seminar 24031 "Fusing Causality, Reasoning, and Learning for Fault Management and Diagnosis."

References

  1. Jonas Ehrhardt, René Heesch, and Oliver Niggemann. Learning process steps as dynamical systems for a sub-symbolic approach of process planning in cyber-physical production systems. In Artificial Intelligence. ECAI 2023 International Workshops, pages 332-345, Cham, 2024. Springer Nature Switzerland. Google Scholar
  2. René Heesch, Alessandro Cimatti, Jonas Ehrhardt, Alexander Diedrich, and Oliver Niggemann. A lazy approach to neural numerical planning with control parameters. In ECAI 2024, pages 4262-4270. IOS Press, 2024. URL: https://doi.org/10.3233/FAIA241000.
  3. René Heesch, Jonas Ehrhardt, and Oliver Niggemann. Integrating machine learning into an SMT-based planning approach for production planning inc yber-physical production systems. In Artificial Intelligence. ECAI 2023 International Workshops, pages 318-331, Cham, 2024. Springer Nature Switzerland. Google Scholar
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