  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.
