An automatically configurable portfolio-based planner with macro-actions: PbP

Abstract

While several powerful domain-independent planners have recently been developed, no one of these clearly outperforms all the others in every known benchmark domain. We present PbP, a multi-planner which automatically configures a portfolio of planners by (i) computing some sets of macro-actions for every planner in the portfolio, (ii) selecting a promising combination of planners in the portfolio and relative useful macro-actions, and (iii) defining some running time slots for their round-robin scheduling during planning. The configuration relies on some knowledge about the performance of the planners in the portfolio and relative macro-actions which is automatically generated from a training problem set. PbP entered the learning track of IPC-2008 and was the overall winner of this competition track. An experimental study confirms the effectiveness of PbP, and shows that the learned configuration knowledge is useful for PbP. Copyright © 2009, Association for the Advancement of Artificial Intelligence. All rights reserved.