2026-09-28T11:51:14Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:27819 2026-09-28T06:35:12Z ddc:004 open_access

FELIX: Model Repair in Numeric Planning Aharoni, Nir Benyamin, Yarin Mohan, Shiwali Stern, Roni Automated diagnosis learning action models model repair 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proposing FELIX, a learning-based repair mechanism that restores planning effectiveness by identifying and correcting outdated numeric effects.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. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Nir Aharoni and Yarin Benyamin and Shiwali Mohan and Roni Stern 2026 Is Part Of OASIcs, Volume 148, 37th International Conference on Principles of Diagnosis and Resilient Systems (DX 2026) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/OASIcs.DX.2026.5 urn:nbn:de:0030-drops-278197 https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.DX.2026.5 eng https://creativecommons.org/licenses/by/4.0/legalcode