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Documents authored by Pill, Ingo

Document
Complete Volume
DOI: 10.4230/OASIcs.DX.2024
OASIcs, Volume 125, DX 2024, Complete Volume

Authors: Ingo Pill, Avraham Natan, and Franz Wotawa

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

Abstract
OASIcs, Volume 125, DX 2024, Complete Volume
Cite as

35th International Conference on Principles of Diagnosis and Resilient Systems (DX 2024). Open Access Series in Informatics (OASIcs), Volume 125, pp. 1-534, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@Proceedings{pill_et_al:OASIcs.DX.2024,
  title =	{{OASIcs, Volume 125, DX 2024, Complete Volume}},
  booktitle =	{35th International Conference on Principles of Diagnosis and Resilient Systems (DX 2024)},
  pages =	{1--534},
  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},
  URN =		{urn:nbn:de:0030-drops-222256},
  doi =		{10.4230/OASIcs.DX.2024},
  annote =	{Keywords: OASIcs, Volume 125, DX 2024, Complete Volume}
}
Document
Front Matter
DOI: 10.4230/OASIcs.DX.2024.0
Front Matter, Table of Contents, Preface, Conference Organization

Authors: Ingo Pill, Avraham Natan, and Franz Wotawa

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

Abstract
Front Matter, Table of Contents, Preface, Conference Organization
Cite as

35th International Conference on Principles of Diagnosis and Resilient Systems (DX 2024). Open Access Series in Informatics (OASIcs), Volume 125, pp. 0:i-0:xvi, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{pill_et_al:OASIcs.DX.2024.0,
  author =	{Pill, Ingo and Natan, Avraham and Wotawa, Franz},
  title =	{{Front Matter, Table of Contents, Preface, Conference Organization}},
  booktitle =	{35th International Conference on Principles of Diagnosis and Resilient Systems (DX 2024)},
  pages =	{0:i--0:xvi},
  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.2024.0},
  URN =		{urn:nbn:de:0030-drops-222242},
  doi =		{10.4230/OASIcs.DX.2024.0},
  annote =	{Keywords: Front Matter, Table of Contents, Preface, Conference Organization}
}
Document
DOI: 10.4230/OASIcs.DX.2024.6
Challenges for Model-Based Diagnosis

Authors: Ingo Pill and Johan de Kleer

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

Abstract
Since the seminal works by Reiter and de Kleer and Williams published in the late 80’s, Model-based Diagnosis has been a significant area of research. This has been motivated by the fact that MBD assists us in tackling a challenge that we face almost on a daily basis, i.e., by MBD allowing us to reason in a structured manner about the root causes for some encountered problem. MBD achieves this in an intuitive, complete and sound way, based on the central idea of investigating the compliance of some observed behavior with a model that describes how a system should behave - given this or that input scenario and parameter set. Over the last 40 years, MBD has been adopted for a multitude of applications, and we saw the emergence of a diverse set of algorithmic, optimizations, as well as extensions to the initial theoretical concepts.We argue that MBD remains highly relevant, with numerous scientific challenges to tackle as we face increasingly complex diagnostic problems. We discuss several such challenges and suggest related topics for PhD theses that have the potential to significantly contribute to the state-of-the-art in MBD research.
Cite as

Ingo Pill and Johan de Kleer. Challenges for Model-Based Diagnosis. In 35th International Conference on Principles of Diagnosis and Resilient Systems (DX 2024). Open Access Series in Informatics (OASIcs), Volume 125, pp. 6:1-6:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{pill_et_al:OASIcs.DX.2024.6,
  author =	{Pill, Ingo and de Kleer, Johan},
  title =	{{Challenges for Model-Based Diagnosis}},
  booktitle =	{35th International Conference on Principles of Diagnosis and Resilient Systems (DX 2024)},
  pages =	{6:1--6:20},
  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.2024.6},
  URN =		{urn:nbn:de:0030-drops-220983},
  doi =		{10.4230/OASIcs.DX.2024.6},
  annote =	{Keywords: Model-based Diagnosis, Diagnosis, Algorithms}
}
Document
DOI: 10.4230/OASIcs.DX.2024.15
Property Learning-Based Fault Detection for Liquid Propellant Rocket Engine Control Systems

Authors: Andrea Urgolo, Ingo Pill, Günther Waxenegger-Wilfing, and Manuel Freiberger

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

Abstract
Accommodating the dynamic and uncertain operational environments that are typical for aerospace applications, our work focuses on robust fault detection and accurate diagnosis in the context of Liquid Propellant Rocket Engines. To this end, we employ techniques based on learning temporal properties which are then dynamically adapted and refined based on observed behavior. Leveraging the capabilities of genetic programming, our methodology evolves and optimizes temporal properties that are validated through formal methods in order to ensure precise, interpretable real-time fault monitoring and diagnosis. Our integrated strategy enables us to enhance resilience, safety and reliability when operating rocket engines - due to the proactive detection and systematic analysis of operational deviations before they would escalate into critical failures. We demonstrate the effectiveness of our method via a rigorous evaluation across varied simulated fault conditions, in order to showcase its potential to significantly mitigate the fault-related risks in aerospace systems.
Cite as

Andrea Urgolo, Ingo Pill, Günther Waxenegger-Wilfing, and Manuel Freiberger. Property Learning-Based Fault Detection for Liquid Propellant Rocket Engine Control Systems. In 35th International Conference on Principles of Diagnosis and Resilient Systems (DX 2024). Open Access Series in Informatics (OASIcs), Volume 125, pp. 15:1-15:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{urgolo_et_al:OASIcs.DX.2024.15,
  author =	{Urgolo, Andrea and Pill, Ingo and Waxenegger-Wilfing, G\"{u}nther and Freiberger, Manuel},
  title =	{{Property Learning-Based Fault Detection for Liquid Propellant Rocket Engine Control Systems}},
  booktitle =	{35th International Conference on Principles of Diagnosis and Resilient Systems (DX 2024)},
  pages =	{15:1--15:20},
  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.2024.15},
  URN =		{urn:nbn:de:0030-drops-221074},
  doi =		{10.4230/OASIcs.DX.2024.15},
  annote =	{Keywords: Machine learning, Runtime verification, Property learning, Monitoring, Fault detection, Diagnosis, Genetic programming, Explainable AI}
}
Document
DOI: 10.4230/DagRep.14.1.25
Fusing Causality, Reasoning, and Learning for Fault Management and Diagnosis (Dagstuhl Seminar 24031)

Authors: Alessandro Cimatti, Ingo Pill, and Alexander Diedrich

Published in: Dagstuhl Reports, Volume 14, Issue 1 (2024)

Abstract
This report documents the program and the outcomes of Dagstuhl Seminar "Fusing Causality, Reasoning, and Learning for Fault Management and Diagnosis" (24031). The goal of this Dagstuhl Seminar was to provide an interdisciplinary forum to discuss the fundamental principles of fault management and diagnosis, bringing together international researchers and practitioners from the fields of symbolic reasoning, machine learning, and control engineering.
Cite as

Alessandro Cimatti, Ingo Pill, and Alexander Diedrich. Fusing Causality, Reasoning, and Learning for Fault Management and Diagnosis (Dagstuhl Seminar 24031). In Dagstuhl Reports, Volume 14, Issue 1, pp. 25-48, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@Article{cimatti_et_al:DagRep.14.1.25,
  author =	{Cimatti, Alessandro and Pill, Ingo and Diedrich, Alexander},
  title =	{{Fusing Causality, Reasoning, and Learning for Fault Management and Diagnosis (Dagstuhl Seminar 24031)}},
  pages =	{25--48},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2024},
  volume =	{14},
  number =	{1},
  editor =	{Cimatti, Alessandro and Pill, Ingo and Diedrich, Alexander},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagRep.14.1.25},
  URN =		{urn:nbn:de:0030-drops-204899},
  doi =		{10.4230/DagRep.14.1.25},
  annote =	{Keywords: cyber-physical systems, diagnosis, fault detection and management, integrative ai, model-based reasoning}
}
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