Online Planner Selection with Graph Neural Networks and Adaptive Scheduling


  • Tengfei Ma IBM Research
  • Patrick Ferber University of Basel
  • Siyu Huo IBM Research
  • Jie Chen MIT-IBM Watson AI Lab
  • Michael Katz IBM Research



Automated planning is one of the foundational areas of AI. Since no single planner can work well for all tasks and domains, portfolio-based techniques have become increasingly popular in recent years. In particular, deep learning emerges as a promising methodology for online planner selection. Owing to the recent development of structural graph representations of planning tasks, we propose a graph neural network (GNN) approach to selecting candidate planners. GNNs are advantageous over a straightforward alternative, the convolutional neural networks, in that they are invariant to node permutations and that they incorporate node labels for better inference.

Additionally, for cost-optimal planning, we propose a two-stage adaptive scheduling method to further improve the likelihood that a given task is solved in time. The scheduler may switch at halftime to a different planner, conditioned on the observed performance of the first one. Experimental results validate the effectiveness of the proposed method against strong baselines, both deep learning and non-deep learning based.

The code is available at




How to Cite

Ma, T., Ferber, P., Huo, S., Chen, J., & Katz, M. (2020). Online Planner Selection with Graph Neural Networks and Adaptive Scheduling. Proceedings of the AAAI Conference on Artificial Intelligence, 34(04), 5077-5084.



AAAI Technical Track: Machine Learning