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FELIX: Model Repair in Numeric Planning

Authors: Nir Aharoni, Yarin Benyamin, Shiwali Mohan, and Roni Stern

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


Abstract
Automated planning algorithms rely on having a sufficiently accurate domain model to solve planning problems, particularly in complex environments with numeric effects. In real-world settings, however, domain models can become misaligned with the environment over time, i.e., no longer accurately reflect the environment. Even a minor drift in numeric effects can lead to faulty plans and execution failures. In this work, we address the problem of repairing numeric planning domain models by proposing FELIX, a learning-based repair mechanism that restores planning effectiveness by identifying and correcting outdated numeric effects. Rather than re-learning the full environment dynamics from scratch, our approach updates only the affected components of the domain model, enabling efficient adaptation with limited data. FELIX restores planning performance across diverse numeric domains, even under complex model drift. It uniquely handles both nonlinear effect shifts and structural signature changes while maintaining polynomial repair overhead under bounded parameter-type assumptions.

Cite as

Nir Aharoni, Yarin Benyamin, Shiwali Mohan, and Roni Stern. FELIX: Model Repair in Numeric Planning. In 37th International Conference on Principles of Diagnosis and Resilient Systems (DX 2026). Open Access Series in Informatics (OASIcs), Volume 148, pp. 5:1-5:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)


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@InProceedings{aharoni_et_al:OASIcs.DX.2026.5,
  author =	{Aharoni, Nir and Benyamin, Yarin and Mohan, Shiwali and Stern, Roni},
  title =	{{FELIX: Model Repair in Numeric Planning}},
  booktitle =	{37th International Conference on Principles of Diagnosis and Resilient Systems (DX 2026)},
  pages =	{5:1--5:18},
  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.2026.5},
  URN =		{urn:nbn:de:0030-drops-278197},
  doi =		{10.4230/OASIcs.DX.2026.5},
  annote =	{Keywords: Automated diagnosis, learning action models, model repair}
}

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