A Survey on Model Repair in AI Planning

Bercher, Pascal; Sreedharan, Sarath; Vallati, Mauro

doi:10.24963/ijcai.2025/1152

A Survey on Model Repair in AI Planning

Pascal Bercher, Sarath Sreedharan, Mauro Vallati

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Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence. Survey Track. Pages 10371-10380.

https://doi.org/10.24963/ijcai.2025/1152

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

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.

Pascal Bercher, Sarath Sreedharan, and Mauro Vallati. 2025. A Survey on Model Repair in AI Planning. In Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-25. IJCAI, 10371–10380. https://doi.org/10.24963/ijcai.2025/1152

@inproceedings{ijcai2025p1152,
  title     = {A Survey on Model Repair in AI Planning},
  author    = {Bercher, Pascal and Sreedharan, Sarath and Vallati, Mauro},
  booktitle = {Proceedings of the Thirty-Fourth International Joint Conference on
               Artificial Intelligence, {IJCAI-25}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  editor    = {James Kwok},
  pages     = {10371--10380},
  year      = {2025},
  month     = {8},
  note      = {Survey Track},
  doi       = {10.24963/ijcai.2025/1152},
  url       = {https://doi.org/10.24963/ijcai.2025/1152},
}

TY  - CPAPER
TI  - A Survey on Model Repair in AI Planning
AU  - Bercher, Pascal
AU  - Sreedharan, Sarath
AU  - Vallati, Mauro
T2  - Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-25
PB  - International Joint Conferences on Artificial Intelligence Organization
PY  - 2025
SP  - 10371
EP  - 10380
DO  - 10.24963/ijcai.2025/1152
UR  - https://doi.org/10.24963/ijcai.2025/1152
ER  -