Automatic Configuration of Sequential Planning Portfolios


  • Jendrik Seipp University of Basel
  • Silvan Sievers University of Basel
  • Malte Helmert University of Basel
  • Frank Hutter University of Freiburg



classical planning, sequential portfolios


Sequential planning portfolios exploit the complementary strengths of different planners. Similarly, automated algorithm configuration tools can customize parameterized planning algorithms for a given type of tasks. Although some work has been done towards combining portfolios and algorithm configuration, the problem of automatically generating a sequential planning portfolio from a parameterized planner for a given type of tasks is still largely unsolved. Here, we present Cedalion, a conceptually simple approach for this problem that greedily searches for the pair of parameter configuration and runtime which, when appended to the current portfolio, maximizes portfolio improvement per additional runtime spent. We show theoretically that Cedalion yields portfolios provably within a constant factor of optimal for the training set distribution. We evaluate Cedalion empirically by applying it to construct sequential planning portfolios based on component planners from the highly parameterized Fast Downward (FD) framework. Results for a broad range of planning settings demonstrate that -- without any knowledge of planning or FD -- Cedalion constructs sequential FD portfolios that rival, and in some cases substantially outperform, manually-built FD portfolios.




How to Cite

Seipp, J., Sievers, S., Helmert, M., & Hutter, F. (2015). Automatic Configuration of Sequential Planning Portfolios. Proceedings of the AAAI Conference on Artificial Intelligence, 29(1).