Explainable Planner Selection for Classical Planning
Abstract
Since no classical planner consistently outperforms all others, it is important to select a planner that works well for a given classical planning task. The two strongest approaches for planner selection use image and graph convolutional neural networks. They have the drawback that the learned models are complicated and uninterpretable. To obtain explainable models, we identify a small set of simple task features and show that elementary and interpretable machine learning techniques can use these features to solve roughly as many tasks as the complex approaches based on neural networks.
Cite
Text
Ferber and Seipp. "Explainable Planner Selection for Classical Planning." AAAI Conference on Artificial Intelligence, 2022. doi:10.1609/AAAI.V36I9.21209Markdown
[Ferber and Seipp. "Explainable Planner Selection for Classical Planning." AAAI Conference on Artificial Intelligence, 2022.](https://mlanthology.org/aaai/2022/ferber2022aaai-explainable/) doi:10.1609/AAAI.V36I9.21209BibTeX
@inproceedings{ferber2022aaai-explainable,
title = {{Explainable Planner Selection for Classical Planning}},
author = {Ferber, Patrick and Seipp, Jendrik},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2022},
pages = {9741-9749},
doi = {10.1609/AAAI.V36I9.21209},
url = {https://mlanthology.org/aaai/2022/ferber2022aaai-explainable/}
}