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DOI: 10.4230/OASIcs.DX.2026.10

Planning Domain Repair Using Preferred Plans and Background Knowledge

Authors: Thomas Eckstein and Gerald Steinbauer-Wagner

Published in: OASIcs, Volume 148, 37th International Conference on Principles of Diagnosis and Resilient Systems (DX 2026)

Abstract

Planning domain repair assists developers with the tedious task of finding and fixing errors in planning domains. We present Domain Repair for PDDL (DrPDDL), a novel automated debugger supporting lifted STRIPS domains with typing. As input, it takes a faulty domain, problem instances with corresponding preferred plans - plans the user desires but which may be invalid in the current domain - and optional State Integrity Axioms (SIAs) that must hold across all reachable states. Unlike prior tools, DrPDDL can also repair domains when the preferred plans are already valid. The debugger uses a pipeline of consecutive algorithms. It first relaxes the domain to fix failing action preconditions and SIA violations, and subsequently constricts it as much as possible while ensuring preferred plan validity. DrPDDL can add or remove positive and negative effects, as well as positive preconditions. We evaluated our debugger by automatically inserting random errors into International Planning Competition domains, generating thousands of test instances. The results demonstrate that DrPDDL successfully detects and repairs a high percentage of these errors.

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Thomas Eckstein and Gerald Steinbauer-Wagner. Planning Domain Repair Using Preferred Plans and Background Knowledge. In 37th International Conference on Principles of Diagnosis and Resilient Systems (DX 2026). Open Access Series in Informatics (OASIcs), Volume 148, pp. 10:1-10:22, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{eckstein_et_al:OASIcs.DX.2026.10,
  author =	{Eckstein, Thomas and Steinbauer-Wagner, Gerald},
  title =	{{Planning Domain Repair Using Preferred Plans and Background Knowledge}},
  booktitle =	{37th International Conference on Principles of Diagnosis and Resilient Systems (DX 2026)},
  pages =	{10:1--10:22},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-455-0},
  ISSN =	{2190-6807},
  year =	{2026},
  volume =	{148},
  editor =	{Pill, Ingo and Zanella, Marina and Provan, Gregory},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.DX.2026.10},
  URN =		{urn:nbn:de:0030-drops-278246},
  doi =		{10.4230/OASIcs.DX.2026.10},
  annote =	{Keywords: Planning, Planning Domain Definition Language (PDDL), Debugging, Preferred Plans, Background Knowledge}
}

Document

DOI: 10.4230/OASIcs.DX.2024.1

A Hierarchical Monitoring and Diagnosis System for Autonomous Robots

Authors: Gerald Steinbauer-Wagner, Leo Fürbaß, Marco De Bortoli, and Louise Travé-Massuyès

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

Abstract

This paper addresses the capability of autonomous robots to achieve flexible goals in dynamic environments. In such a setting numerous challenges jeopardize the robustness of such systems. Thus, we propose a hierarchical diagnosis concept for layered control architectures, that can detect and deal with such challenges to maintain a consistent knowledge about the world and to allow reliable decision-making. Layered control systems use various knowledge representations and decision-making mechanisms teamed with specialized isolated fault-handling approaches. However, some issues can only be identified if the information from different layers is combined. Our approach addresses challenges like failing actions, uncertain observations, and unmodeled events by propagating observations and diagnoses results throughout the hierarchy. This enhances adaptability and dependability in various domains. In this paper, we present a prototype architecture following this approach.

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Gerald Steinbauer-Wagner, Leo Fürbaß, Marco De Bortoli, and Louise Travé-Massuyès. A Hierarchical Monitoring and Diagnosis System for Autonomous Robots. In 35th International Conference on Principles of Diagnosis and Resilient Systems (DX 2024). Open Access Series in Informatics (OASIcs), Volume 125, pp. 1:1-1:9, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{steinbauerwagner_et_al:OASIcs.DX.2024.1,
  author =	{Steinbauer-Wagner, Gerald and F\"{u}rba{\ss}, Leo and De Bortoli, Marco and Trav\'{e}-Massuy\`{e}s, Louise},
  title =	{{A Hierarchical Monitoring and Diagnosis System for Autonomous Robots}},
  booktitle =	{35th International Conference on Principles of Diagnosis and Resilient Systems (DX 2024)},
  pages =	{1:1--1:9},
  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.2024.1},
  URN =		{urn:nbn:de:0030-drops-220938},
  doi =		{10.4230/OASIcs.DX.2024.1},
  annote =	{Keywords: Cognitive Architecture, Autonomous Agents, Dependability, Hierarchical Monitoring and Diagnosis}
}

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Documents authored by Steinbauer-Wagner, Gerald

Planning Domain Repair Using Preferred Plans and Background Knowledge

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A Hierarchical Monitoring and Diagnosis System for Autonomous Robots

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