Choosing a Classical Planner with Graph Neural Networks
<|reference_start|>Choosing a Classical Planner with Graph Neural Networks: Online planner selection is the task of choosing a solver out of a predefined set for a given planning problem. As planning is computationally hard, the performance of solvers varies greatly on planning problems. Thus, the ability to predict their performance on a given problem is of great importance. While a variety of learning methods have been employed, for classical cost-optimal planning the prevailing approach uses Graph Neural Networks (GNNs). In this work, we continue the line of work on using GNNs for online planner selection. We perform a thorough investigation of the impact of the chosen GNN model, graph representation and node features, as well as prediction task. Going further, we propose using the graph representation obtained by a GNN as an input to the Extreme Gradient Boosting (XGBoost) model, resulting in a more resource-efficient yet accurate approach. We show the effectiveness of a variety of GNN-based online planner selection methods, opening up new exciting avenues for research on online planner selection.<|reference_end|>
@article{vatter2024choosing,
    title={Choosing a Classical Planner with Graph Neural Networks},
    author={Jana Vatter, Ruben Mayer, Hans-Arno Jacobsen, Horst Samulowitz,
  Michael Katz},
    journal={arXiv preprint arXiv:2402.04874},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.04874},
    primaryClass={cs.AI cs.LG}
}