Deep Learning for Cost-Optimal Planning: Task-Dependent Planner Selection


  • Silvan Sievers University of Basel
  • Michael Katz IBM Research
  • Shirin Sohrabi IBM
  • Horst Samulowitz IBM Research
  • Patrick Ferber University of Basel



As classical planning is known to be computationally hard, no single planner is expected to work well across many planning domains. One solution to this problem is to use online portfolio planners that select a planner for a given task. These portfolios perform a classification task, a well-known and wellresearched task in the field of machine learning. The classification is usually performed using a representation of planning tasks with a collection of hand-crafted statistical features. Recent techniques in machine learning that are based on automatic extraction of features have not been employed yet due to the lack of suitable representations of planning tasks.

In this work, we alleviate this barrier. We suggest representing planning tasks by images, allowing to exploit arguably one of the most commonly used and best developed techniques in deep learning. We explore some of the questions that inevitably rise when applying such a technique, and present various ways of building practically useful online portfoliobased planners. An evidence of the usefulness of our proposed technique is a planner that won the cost-optimal track of the International Planning Competition 2018.




How to Cite

Sievers, S., Katz, M., Sohrabi, S., Samulowitz, H., & Ferber, P. (2019). Deep Learning for Cost-Optimal Planning: Task-Dependent Planner Selection. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 7715-7723.



AAAI Technical Track: Planning, Routing, and Scheduling