Towards Automated Modeling Assistance: An Efficient Approach for Repairing Flawed Planning Domains
Abstract
Designing a planning domain is a difficult task in AI planning. Assisting tools are thus required if we want planning to be used more broadly. In this paper, we are interested in automatically correcting a flawed domain. In particular, we are concerned with the scenario where a domain contradicts a plan that is known to be valid. Our goal is to repair the domain so as to turn the plan into a solution. Specifically, we consider both grounded and lifted representations support for negative preconditions and show how to explore the space of repairs to find the optimal one efficiently. As an evidence of the efficiency of our approach, the experiment results show that all flawed domains except one in the benchmark set can be repaired optimally by our approach within one second.
Cite
Text
Lin et al. "Towards Automated Modeling Assistance: An Efficient Approach for Repairing Flawed Planning Domains." AAAI Conference on Artificial Intelligence, 2023. doi:10.1609/AAAI.V37I10.26418Markdown
[Lin et al. "Towards Automated Modeling Assistance: An Efficient Approach for Repairing Flawed Planning Domains." AAAI Conference on Artificial Intelligence, 2023.](https://mlanthology.org/aaai/2023/lin2023aaai-automated/) doi:10.1609/AAAI.V37I10.26418BibTeX
@inproceedings{lin2023aaai-automated,
title = {{Towards Automated Modeling Assistance: An Efficient Approach for Repairing Flawed Planning Domains}},
author = {Lin, Songtuan and Grastien, Alban and Bercher, Pascal},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2023},
pages = {12022-12031},
doi = {10.1609/AAAI.V37I10.26418},
url = {https://mlanthology.org/aaai/2023/lin2023aaai-automated/}
}