Told You That Will Not Work: Optimal Corrections to Planning Domains Using Counter-Example Plans. Lin, S., Grastien, A., Shome, R., & Bercher, P. In Proceedings of the 39th AAAI Conference on Artificial Intelligence (AAAI 2025), pages 26596–26604, 2025. AAAI. ![Told You That Will Not Work: Optimal Corrections to Planning Domains Using Counter-Example Plans [pdf]](https://bibbase.org/img/filetypes/pdf.svg "pdf")
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Zenodo doi abstract bibtex 10 downloads Hardness of modeling a planning domain is a major obstacle for making automated planning techniques accessible. We develop a tool that helps modelers correct domains based on available information such as the known feasibility or infeasibility of certain plans. Designing model repair strategies that are capable of repairing flawed planning domains automatically has been explored in previous work to use positive plans (invalid in the given (flawed) domain but feasible in the “true” domain). In this work, we highlight the importance of and study counter-example negative plans (valid in the given (flawed) domain but infeasible in the “true” domain). Our approach automatically corrects a domain by finding an optimal repair set to the domain which turns all negative plans into non-solutions, in addition to making all positive plans solutions. Experiments indicate strong performance in the fast-downward benchmark suite with random errors. A handcrafted benchmark with domain flaws inspired by some practical applications also motivates the method’s efficacy.
@InProceedings{Lin2025CounterExamplePlans,
author = {Songtuan Lin and Alban Grastien and Rahul Shome and Pascal Bercher},
booktitle = {Proceedings of the 39th AAAI Conference on Artificial Intelligence (AAAI 2025)},
title = {Told You That Will Not Work: Optimal Corrections to Planning Domains Using Counter-Example Plans},
year = {2025},
publisher = {AAAI},
abstract = {Hardness of modeling a planning domain is a major obstacle for making automated planning techniques accessible. We develop a tool that helps modelers correct domains based on available information such as the known feasibility or infeasibility of certain plans. Designing model repair strategies that are capable of repairing flawed planning domains automatically has been explored in previous work to use positive plans (invalid in the given (flawed) domain but feasible in the “true” domain). In this work, we highlight the importance of and study counter-example negative plans (valid in the given (flawed) domain but infeasible in the “true” domain). Our approach automatically corrects a domain by finding an optimal repair set to the domain which turns all negative plans into non-solutions, in addition to making all positive plans solutions. Experiments indicate strong performance in the fast-downward benchmark suite with random errors. A handcrafted benchmark with domain flaws inspired by some practical applications also motivates the method’s efficacy.},
keywords = {conference,DECRA},
pages = {26596--26604},
doi = {10.1609/aaai.v39i25.34861},
url_Paper = {https://bercher.net/publications/2025/Lin2025CounterExamplePlans.pdf},
url_zenodo = {https://zenodo.org/records/14533200}
}
Downloads: 10
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