---
title: "A Survey on Model Repair in AI Planning"
authors:
  - "Pascal Bercher"
  - "Sarath Sreedharan"
  - "Mauro Vallati"
doi: "10.24963/ijcai.2025/1152"
url: "https://ijcai.org/proceedings/2025/1152"
pdf: "https://ijcai.org/proceedings/2025/1152.pdf"
venue: "Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence"
year: 2025
pages: "10371-10380"
track: "Survey Track"
keywords:
  - "Planning and Scheduling: PS: Hierarchical planning"
  - "Knowledge Representation and Reasoning: KRR: Learning and reasoning"
  - "Planning and Scheduling: PS: Model-based reasoning"
  - "Knowledge Representation and Reasoning: KRR: Reasoning about actions"
---

# A Survey on Model Repair in AI Planning

Pascal Bercher, Sarath Sreedharan, Mauro Vallati

*Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence*, pages 10371-10380. DOI: [10.24963/ijcai.2025/1152](https://doi.org/10.24963/ijcai.2025/1152)

## Abstract

Accurate planning models are a prerequisite for the appropriate functioning of AI planning applications. Creating these models is, however, a tedious and error-prone task -- even for planning experts. This makes the provision of automated modeling support essential. In this work, we differentiate between approaches that learn models from scratch (called domain model acquisition) and those that repair flawed or incomplete ones. We survey approaches for the latter, including those that can be used for domain repair but have been developed for other applications, discuss possible optimization metrics (i.e., which repaired model to aim at), and conclude with lines of research we believe deserve more attention.