Explainable Planner Selection for Classical Planning
DOI:
https://doi.org/10.1609/aaai.v36i9.21209Keywords:
Planning, Routing, And Scheduling (PRS)Abstract
Since no classical planner consistently outperforms all others, 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 neural networks. They have the drawback that the learned models are complicated and uninterpretable. To obtain explainable models, we identify a small set of simple task features and show that elementary and interpretable machine learning techniques can use these features to solve roughly as many tasks as the complex approaches based on neural networks.Downloads
Published
2022-06-28
How to Cite
Ferber, P., & Seipp, J. (2022). Explainable Planner Selection for Classical Planning. Proceedings of the AAAI Conference on Artificial Intelligence, 36(9), 9741-9749. https://doi.org/10.1609/aaai.v36i9.21209
Issue
Section
AAAI Technical Track on Planning, Routing, and Scheduling