Contributed Talk
in
Workshop: Generalization in Planning (GenPlan '23)
GOOSE: Learning Domain-Independent Heuristics
Dillon Chen · Felipe Trevizan · Sylvie Thiebaux
Keywords: [ classical planning ] [ learning for planning ] [ generalised planning ] [ lifted planning ]
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.