Name
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 empirical 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. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1142/S0218213017600065 | INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS |
Keywords | Field | DocType |
Automated planning, optimal planning, sequential portfolio, per-instance portfolio generation | Mathematical optimization,Satisficing,Application portfolio management,Computer science,Optimal planning,Planner,Supervised learning,Portfolio,Empirical algorithmics,Automated planning and scheduling | Journal |
Volume | Issue | ISSN |
26 | 1 | 0218-2130 |
Citations | PageRank | References |
3 | 0.40 | 13 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mattia Rizzini | 1 | 4 | 0.74 |
| Chris Fawcett | 2 | 66 | 5.36 |
| Mauro Vallati | 3 | 216 | 46.63 |
| Alfonso Gerevini | 4 | 1424 | 100.21 |
| Holger H. Hoos | 5 | 5327 | 308.70 |