Details
| Original language | English |
|---|---|
| Title of host publication | International Joint Conference on Artificial Intelligence (IJCAI 2017) |
| Editors | Carles Sierra |
| Pages | 5025-5029 |
| Number of pages | 5 |
| ISBN (electronic) | 9780999241103 |
| Publication status | Published - 2017 |
| Externally published | Yes |
| Event | 26th International Joint Conference on Artificial Intelligence, IJCAI 2017 - Melbourne, Australia Duration: 19 Aug 2017 → 25 Aug 2017 |
Abstract
Algorithm selection (AS) techniques - which involve choosing from a set of algorithms the one expected to solve a given problem instance most efficiently - have substantially improved the state of the art in solving many prominent AI problems, such as SAT, CSP, ASP, MAXSAT and QBF. Although several AS procedures have been introduced, not too surprisingly, none of them dominates all others across all AS scenarios. Furthermore, these procedures have parameters whose optimal values vary across AS scenarios. In this extended abstract of our 2015 JAIR article of the same title, we summarize AUTOFOLIO, which uses an algorithm configuration procedure to automatically select an AS approach and optimize its parameters for a given AS scenario. AUTOFOLIO allows researchers and practitioners across a broad range of applications to exploit the combined power of many different AS methods and to automatically construct high-performance algorithm selectors. We demonstrate that AUTOFOLIO was able to produce new state-of-the-art algorithm selectors for 7 well-studied AS scenarios and matches state-oftheart performance statistically on all other scenarios. Compared to the best single algorithm for each AS scenario, AUTOFOLIO achieved average speedup factors between 1:3 and 15:4.
ASJC Scopus subject areas
- Computer Science(all)
- Artificial Intelligence
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International Joint Conference on Artificial Intelligence (IJCAI 2017). ed. / Carles Sierra. 2017. p. 5025-5029.
Research output: Chapter in book/report/conference proceeding › Conference abstract › Research › peer review
}
TY - CHAP
T1 - AutoFolio: An Automatically Configured Algorithm Selector (Extended Abstract)
AU - Lindauer, Marius
AU - Hutter, Frank
AU - Hoos, Holger H.
AU - Schaub, Torsten
PY - 2017
Y1 - 2017
N2 - Algorithm selection (AS) techniques - which involve choosing from a set of algorithms the one expected to solve a given problem instance most efficiently - have substantially improved the state of the art in solving many prominent AI problems, such as SAT, CSP, ASP, MAXSAT and QBF. Although several AS procedures have been introduced, not too surprisingly, none of them dominates all others across all AS scenarios. Furthermore, these procedures have parameters whose optimal values vary across AS scenarios. In this extended abstract of our 2015 JAIR article of the same title, we summarize AUTOFOLIO, which uses an algorithm configuration procedure to automatically select an AS approach and optimize its parameters for a given AS scenario. AUTOFOLIO allows researchers and practitioners across a broad range of applications to exploit the combined power of many different AS methods and to automatically construct high-performance algorithm selectors. We demonstrate that AUTOFOLIO was able to produce new state-of-the-art algorithm selectors for 7 well-studied AS scenarios and matches state-oftheart performance statistically on all other scenarios. Compared to the best single algorithm for each AS scenario, AUTOFOLIO achieved average speedup factors between 1:3 and 15:4.
AB - Algorithm selection (AS) techniques - which involve choosing from a set of algorithms the one expected to solve a given problem instance most efficiently - have substantially improved the state of the art in solving many prominent AI problems, such as SAT, CSP, ASP, MAXSAT and QBF. Although several AS procedures have been introduced, not too surprisingly, none of them dominates all others across all AS scenarios. Furthermore, these procedures have parameters whose optimal values vary across AS scenarios. In this extended abstract of our 2015 JAIR article of the same title, we summarize AUTOFOLIO, which uses an algorithm configuration procedure to automatically select an AS approach and optimize its parameters for a given AS scenario. AUTOFOLIO allows researchers and practitioners across a broad range of applications to exploit the combined power of many different AS methods and to automatically construct high-performance algorithm selectors. We demonstrate that AUTOFOLIO was able to produce new state-of-the-art algorithm selectors for 7 well-studied AS scenarios and matches state-oftheart performance statistically on all other scenarios. Compared to the best single algorithm for each AS scenario, AUTOFOLIO achieved average speedup factors between 1:3 and 15:4.
UR - http://www.scopus.com/inward/record.url?scp=85031897375&partnerID=8YFLogxK
M3 - Conference abstract
SP - 5025
EP - 5029
BT - International Joint Conference on Artificial Intelligence (IJCAI 2017)
A2 - Sierra, Carles
T2 - 26th International Joint Conference on Artificial Intelligence, IJCAI 2017
Y2 - 19 August 2017 through 25 August 2017
ER -