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Documents authored by Heesch, René

Document
DOI: 10.4230/OASIcs.DX.2024.7
Design Principles for Falsifiable, Replicable and Reproducible Empirical Machine Learning Research

Authors: Daniel Vranješ, Jonas Ehrhardt, René Heesch, Lukas Moddemann, Henrik Sebastian Steude, and Oliver Niggemann

Published in: OASIcs, Volume 125, 35th International Conference on Principles of Diagnosis and Resilient Systems (DX 2024)

Abstract
Machine learning is becoming increasingly important in the diagnosis and planning fields, where data-driven models and algorithms are being employed as alternatives to traditional first-principle approaches. Empirical research plays a fundamental role in the machine learning domain. At the heart of impactful empirical research lies the development of clear research hypotheses, which then shape the design of experiments. The execution of experiments must be carried out with precision to ensure reliable results, followed by statistical analysis to interpret these outcomes. This process is key to either supporting or refuting initial hypotheses. Despite its importance, there is a high variability in research practices across the machine learning community and no uniform understanding of quality criteria for empirical research. To address this gap, we propose a model for the empirical research process, accompanied by guidelines to uphold the validity of empirical research. By embracing these recommendations, greater consistency, enhanced reliability and increased impact can be achieved.
Cite as

Daniel Vranješ, Jonas Ehrhardt, René Heesch, Lukas Moddemann, Henrik Sebastian Steude, and Oliver Niggemann. Design Principles for Falsifiable, Replicable and Reproducible Empirical Machine Learning Research. In 35th International Conference on Principles of Diagnosis and Resilient Systems (DX 2024). Open Access Series in Informatics (OASIcs), Volume 125, pp. 7:1-7:13, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{vranjes_et_al:OASIcs.DX.2024.7,
  author =	{Vranje\v{s}, Daniel and Ehrhardt, Jonas and Heesch, Ren\'{e} and Moddemann, Lukas and Steude, Henrik Sebastian and Niggemann, Oliver},
  title =	{{Design Principles for Falsifiable, Replicable and Reproducible Empirical Machine Learning Research}},
  booktitle =	{35th International Conference on Principles of Diagnosis and Resilient Systems (DX 2024)},
  pages =	{7:1--7:13},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-356-0},
  ISSN =	{2190-6807},
  year =	{2024},
  volume =	{125},
  editor =	{Pill, Ingo and Natan, Avraham and Wotawa, Franz},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.DX.2024.7},
  URN =		{urn:nbn:de:0030-drops-220991},
  doi =		{10.4230/OASIcs.DX.2024.7},
  annote =	{Keywords: machine learning, hypothesis design, research design, experimental research, statistical testing, diagnosis, planning}
}
Document
DOI: 10.4230/OASIcs.DX.2024.9
Inferring Sensor Placement Using Critical Pairs and Satisfiability Modulo Theory

Authors: Alexander Diedrich, René Heesch, Marco Bozzano, Björn Ludwig, Alessandro Cimatti, and Oliver Niggemann

Published in: OASIcs, Volume 125, 35th International Conference on Principles of Diagnosis and Resilient Systems (DX 2024)

Abstract
Industrial fault diagnosis exhibits the perennial problem of reasoning with partial and real-valued information. This is mainly due to the fact that in real-world applications, industrial systems are only instrumented insofar, as sensor information is required for their functioning. However, such instrumentation leaves out much information that would be useful for fault diagnosis. This is problematic since consistency-based fault diagnosis uses available information and computes intermediate values within a system description. These values are then used to compare expected normal behaviour to actual observed values. In the past, this was done only for Boolean circuits. Recently, satisfiability modulo non-linear arithmetic (SMT) formulations have been developed that allow the calculation of real values, instead of only Boolean ones. Leveraging those formulations, we in this article present a novel method to infer missing sensor values using an SMT system description and the notion of critical pairs. We show on a running example and also empirically that we can infer novel measurements for five process industrial systems. We conclude that, although SMT calculations accumulate some error, we can infer novel optimal measurements for all systems.
Cite as

Alexander Diedrich, René Heesch, Marco Bozzano, Björn Ludwig, Alessandro Cimatti, and Oliver Niggemann. Inferring Sensor Placement Using Critical Pairs and Satisfiability Modulo Theory. In 35th International Conference on Principles of Diagnosis and Resilient Systems (DX 2024). Open Access Series in Informatics (OASIcs), Volume 125, pp. 9:1-9:19, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{diedrich_et_al:OASIcs.DX.2024.9,
  author =	{Diedrich, Alexander and Heesch, Ren\'{e} and Bozzano, Marco and Ludwig, Bj\"{o}rn and Cimatti, Alessandro and Niggemann, Oliver},
  title =	{{Inferring Sensor Placement Using Critical Pairs and Satisfiability Modulo Theory}},
  booktitle =	{35th International Conference on Principles of Diagnosis and Resilient Systems (DX 2024)},
  pages =	{9:1--9:19},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-356-0},
  ISSN =	{2190-6807},
  year =	{2024},
  volume =	{125},
  editor =	{Pill, Ingo and Natan, Avraham and Wotawa, Franz},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.DX.2024.9},
  URN =		{urn:nbn:de:0030-drops-221013},
  doi =		{10.4230/OASIcs.DX.2024.9},
  annote =	{Keywords: Sensor Placement, Satisfiability Modulo Theory, Critical Pairs, Diagnosability}
}
Document
Extended Abstract
DOI: 10.4230/OASIcs.DX.2024.32
Summary of "A Lazy Approach to Neural Numerical Planning with Control Parameters" (Extended Abstract)

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

Published in: OASIcs, Volume 125, 35th International Conference on Principles of Diagnosis and Resilient Systems (DX 2024)

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.
Cite as

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)

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@InProceedings{heesch_et_al:OASIcs.DX.2024.32,
  author =	{Heesch, Ren\'{e} and Cimatti, Alessandro and Ehrhardt, Jonas and Diedrich, Alexander and Niggemann, Oliver},
  title =	{{Summary of "A Lazy Approach to Neural Numerical Planning with Control Parameters"}},
  booktitle =	{35th International Conference on Principles of Diagnosis and Resilient Systems (DX 2024)},
  pages =	{32:1--32:3},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-356-0},
  ISSN =	{2190-6807},
  year =	{2024},
  volume =	{125},
  editor =	{Pill, Ingo and Natan, Avraham and Wotawa, Franz},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.DX.2024.32},
  URN =		{urn:nbn:de:0030-drops-221243},
  doi =		{10.4230/OASIcs.DX.2024.32},
  annote =	{Keywords: Satisfiability Modulo Theory, Neural Numerical Planning with Control Parameters, Neural Networks}
}
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