Explainable Planner Selection

Since no classical planner consistently outperforms all oth ers, it is important to select a planner that works well for a given classical planning task. The two strongest approaches for planner selection use image and graph convolutional neu ral networks. They have the drawback that the learned mod els are not interpretable. To obtain explainable models, we identify a small set of simple task features and show that el ementary and interpretable machine learning techniques can use these features to solve as many tasks as the approaches based on neural networks.