  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.
