Learning Domain-Independent Heuristics for Grounded and Lifted Planning
Abstract
We present three novel graph representations of planning tasks suitable for learning domain-independent heuristics using Graph Neural Networks (GNNs) to guide search. In particular, to mitigate the issues caused by large grounded GNNs we present the first method for learning domain-independent heuristics with only the lifted representation of a planning task. We also provide a theoretical analysis of the expressiveness of our models, showing that some are more powerful than STRIPS-HGN, the only other existing model for learning domain-independent heuristics. Our experiments show that our heuristics generalise to much larger problems than those in the training set, vastly surpassing STRIPS-HGN heuristics.
Cite
Text
Chen et al. "Learning Domain-Independent Heuristics for Grounded and Lifted Planning." AAAI Conference on Artificial Intelligence, 2024. doi:10.1609/AAAI.V38I18.29986Markdown
[Chen et al. "Learning Domain-Independent Heuristics for Grounded and Lifted Planning." AAAI Conference on Artificial Intelligence, 2024.](https://mlanthology.org/aaai/2024/chen2024aaai-learning-c/) doi:10.1609/AAAI.V38I18.29986BibTeX
@inproceedings{chen2024aaai-learning-c,
title = {{Learning Domain-Independent Heuristics for Grounded and Lifted Planning}},
author = {Chen, Dillon Ze and Thiébaux, Sylvie and Trevizan, Felipe W.},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2024},
pages = {20078-20086},
doi = {10.1609/AAAI.V38I18.29986},
url = {https://mlanthology.org/aaai/2024/chen2024aaai-learning-c/}
}