Abstract
Combining the complementary strengths of several algorithms through portfolio approaches has been demonstrated to be effective in solving a wide range of AI problems. Notably, portfolio techniques have been prominently applied to suboptimal (satisficing) AI planning. Here, we consider the construction of sequential planner portfolios for (domain-independent) optimal planning. Specifically, we introduce four techniques (three of which are dynamic) for per-instance planner schedule generation using problem instance features, and investigate the usefulness of a range of static and dynamic techniques for combining planners. Our extensive experimental analysis demonstrates the benefits of using static and dynamic sequential portfolios for optimal planning, and provides insights on the most suitable conditions for their fruitful exploitation.
Original language | English |
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Title of host publication | 2015 IEEE 27th International Conference on Tools with Artificial Intelligence |
Publisher | IEEE |
Pages | 494-501 |
Number of pages | 8 |
ISBN (Electronic) | 9781509001637, 9781509001620 |
DOIs | |
Publication status | Published - 7 Jan 2016 |
Event | 2015 IEEE 27th International Conference on Tools with Artificial Intelligence - Vietri sul Mare, Italy Duration: 9 Nov 2015 → 11 Nov 2015 Conference number: 27 |
Conference
Conference | 2015 IEEE 27th International Conference on Tools with Artificial Intelligence |
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Abbreviated title | ICTAI |
Country/Territory | Italy |
City | Vietri sul Mare |
Period | 9/11/15 → 11/11/15 |