@inproceedings{
chen2023goose,
title={{GOOSE}: Learning Domain-Independent Heuristics},
author={Dillon Ze Chen and Sylvie Thiebaux and Felipe Trevizan},
booktitle={NeurIPS 2023 Workshop on Generalization in Planning},
year={2023},
url={https://openreview.net/forum?id=cTtMNEn2Kr}
}
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.
GOOSE: Learning Domain-Independent Heuristics
Dillon Ze Chen Sylvie Thiebaux Felipe Trevizan