Data Entry: Please note that the research database will be replaced by UNIverse by the end of October 2023. Please enter your data into the system https://universe-intern.unibas.ch. Thanks
Automatic configuration of sequential planning portfolios
Book title (Conference Proceedings)
Proceedings of the 29th AAAI Conference on Artificial Intelligence (AAAI 2015) : January 25 –30, 2015, Austin, Texas, USA
Place of Conference
Austin, Texas
Publisher
AAAI Press
Place of Publication
Palo Alto, Calif.
Pages
3364-3370
Abstract
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 plan- ner 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 h parameter configu- ration, runtime i pair 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 port- folios that rival, and in some cases substantially outperform, manually-built FD portfolios.