Current approaches for learning for planning have yet to achieve competitive
performance against classical planners in several domains, and have poor
overall performance. In this work, we construct novel graph representations of
lifted planning tasks and use the WL algorithm to generate features from them.
These features are used with classical machine learning methods which have up
to 2 orders of magnitude fewer parameters and train up to 3 orders of magnitude
faster than the state-of-the-art deep learning for planning models. Our novel
approach, WL-GOOSE, reliably learns heuristics from scratch and outperforms the
$h^{\text{FF}}$ heuristic in a fair competition setting. It also outperforms or
ties with LAMA on 4 out of 10 domains on coverage and 7 out of 10 domains on
plan quality. WL-GOOSE is the first learning for planning model which achieves
these feats. Furthermore, we study the connections between our novel WL feature
generation method, previous theoretically flavoured learning architectures, and
Description Logic Features for planning.

当前的学习规划方法在几个领域中仍无法与经典规划器达到竞争力的性能，并具有较差的综合性能。本研究提出了一种构建新颖的图表示的提升规划任务，并使用 WL 算法从中生成特征的方法。这些特征与传统机器学习方法结合使用，其参数数量比最先进的深度学习规划模型少 2 个数量级，训练速度快 3 个数量级。我们的新方法 WL-GOOSE 可可靠地从头开始学习启发式，并在公平竞争环境中优于 $h^{ext {FF}}$ 启发式。在 10 个领域中 WL-GOOSE 在覆盖率上优于或与 LAMA 持平，在计划质量上优于 LAMA 的 4 个领域。WL-GOOSE 是第一个在学习规划模型中取得这些成就的模型。此外，我们还研究了我们的新型 WL 特征生成方法、先前的理论化学习架构和用于规划的描述逻辑特征之间的联系。

回归传统：用传统机器学习学习可靠的启发式方法

Return to Tradition: Learning Reliable Heuristics with Classical Machine  Learning

This work generalizes graph neural networks (GNNs) beyond those based on the
Weisfeiler-Lehman (WL) algorithm, graph Laplacians, and diffusions. Our
approach, denoted Relational Pooling (RP), draws from the theory of finite
partial exchangeability to provide a framework with maximal representation
power for graphs. RP can work with existing graph representation models and,
somewhat counterintuitively, can make them even more powerful than the original
WL isomorphism test. Additionally, RP allows architectures like Recurrent
Neural Networks and Convolutional Neural Networks to be used in a theoretically
sound approach for graph classification. We demonstrate improved performance of
RP-based graph representations over state-of-the-art methods on a number of
tasks.

本文提出了一种基于关系池化（RP）的图神经网络模型，可以进一步提高现有的图表示模型的表征能力，并且在图分类任务上实现了超越现有方法的性能表现