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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}
}

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Planning Domain Repair Using Preferred Plans and Background Knowledge

Abstract

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