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.

翻译：我们提出了三种适用于规划任务的新颖图表示，通过图神经网络（GNN）学习领域无关的启发式方法来指导搜索。特别地，为缓解大规模基态GNN带来的问题，我们首次提出仅利用规划任务的提升表示来学习领域无关启发式方法。我们还对所提模型的表达能力进行了理论分析，证明部分模型比STRIPS-HGN（现有唯一的领域无关启发式学习模型）更具表达力。实验表明，我们的启发式方法在远大于训练集的问题上具备泛化能力，其性能显著超越STRIPS-HGN启发式方法。