Graph Neural Networks and Graph Kernels For Learning Heuristics: Is there a difference?

Published: 28 Oct 2023, Last Modified: 04 Dec 2023GenPlan'23EveryoneRevisionsBibTeX
Abstract: Graph neural networks (GNNs) have been used in various works for learning heuristics to guide search for planning. However, they are hindered by their slow evaluation speed and their limited expressiveness. It is also a known fact that the expressiveness of common GNNs is bounded by the Weisfeiler-Lehman (WL) algorithm for testing graph isomorphism, with which one can generate features for graphs. Thus, one may ask how do GNNs compare against machine learning models operating on WL features of planning problems represented as graphs? Our experiments show that linear models with WL features outpeform GNN models for learning heuristics for planning in the learning track of the 2023 International Planning Competition (IPC). Most notably, our model WL-GOOSE is the first model in the learning for planning literature which can reliably learn heuristics from scratch that are competitive with $h^{\text{FF}}$ on problem sizes much larger than those seen in the training set.
Submission Number: 4