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.

我们提出了三种新颖的图表示方法，用于使用图神经网络（GNN）指导搜索，以学习与领域无关的启发式。我们还提供了对我们模型表达能力的理论分析，证明其中一些比 STRIPS-HGN（唯一的其他现有的学习领域无关启发式模型）更强大。我们的实验表明，我们的启发式算法可以广泛适用于训练集之外的更大问题，并远远超过 STRIPS-HGN 的启发式算法。