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arXiv · 2312.11143

Learning Domain-Independent Heuristics for Grounded and Lifted Planning

Dillon Z. Chen·Sylvie Thiébaux·Felipe Trevizan
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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.

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Dillon Z. Chen, Sylvie Thiébaux, Felipe Trevizan. 2023-12-18. Learning Domain-Independent Heuristics for Grounded and Lifted Planning. https://doi.org/10.1609/aaai.v38i18.29986

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