Learning Portfolios of Automatically Tuned Planners


  • Jendrik Seipp Albert-Ludwigs-University Freiburg
  • Manuel Braun Albert-Ludwigs-Universiy Freiburg
  • Johannes Garimort Albert-Ludwigs-University Freiburg
  • Malte Helmert University of Basel




portfolios, parameter tuning, classical planning, heuristic search


Portfolio planners and parameter tuning are two ideas that have recently attracted significant attention in the domain-independent planning community. We combine these two ideas and present a portfolio planner that runs automatically configured planners. We let the automatic parameter tuning framework ParamILS find fast configurations of the Fast Downward planning system for a number of planning domains. Afterwards we learn a portfolio of those planner configurations. Evaluation of our portfolio planner on the IPC 2011 domains shows that it has a significantly higher IPC score than the winner of the sequential satisficing track.




How to Cite

Seipp, J., Braun, M., Garimort, J., & Helmert, M. (2012). Learning Portfolios of Automatically Tuned Planners. Proceedings of the International Conference on Automated Planning and Scheduling, 22(1), 368-372. https://doi.org/10.1609/icaps.v22i1.13538