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.

翻译：当前用于规划的深度学习方法在多个领域中尚未达到与经典规划器相媲美的竞争性能，且整体表现欠佳。本研究通过构建提升式规划任务的新型图表示，并利用WL算法从中生成特征。这些特征结合具有比最先进深度学习规划模型少2个数量级参数、训练速度快3个数量级的经典机器学习方法。我们提出的新型方法WL-GOOSE能够从零可靠地学习启发式方法，并在公平竞争条件下优于$h^{\text{FF}}$启发式方法。在覆盖率和规划质量指标上，该方法在10个领域中的4个领域超越或追平LAMA，在10个领域中的7个领域超越或追平LAMA。WL-GOOSE是首个实现此成就的规划学习方法。此外，我们研究了我们提出的新型WL特征生成方法、以往具有理论色彩的架构以及规划中的描述逻辑特征之间的关联。