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Contributed Talk
Workshop: Generalization in Planning (GenPlan '23)

GOOSE: Learning Domain-Independent Heuristics

Dillon Chen · Felipe Trevizan · Sylvie Thiebaux

Keywords: [ lifted planning ] [ generalised planning ] [ learning for planning ] [ classical planning ]

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Sat 16 Dec 9:05 a.m. PST — 9:15 a.m. PST


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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