Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | Algorithm Configuration |
| Untertitel | papers presented at the Twenty-Ninth AAAI Conference on Artificial Intelligence |
| Herausgeber (Verlag) | AI Access Foundation |
| Seiten | 9-15 |
| Seitenumfang | 7 |
| ISBN (elektronisch) | 9781577357124 |
| Publikationsstatus | Veröffentlicht - 2015 |
| Extern publiziert | Ja |
| Veranstaltung | 29th AAAI Conference on Artificial Intelligence, AAAI 2015 - Austin, USA / Vereinigte Staaten Dauer: 25 Jan. 2015 → 30 Jan. 2015 |
Publikationsreihe
| Name | AAAI Workshop - Technical Report |
|---|
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 QBE 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. This holds specifically for the machine learning techniques that form the core of current AS procedures and for their hyperparameters. Therefore, to successfully apply AS to new problems, algorithms and benchmark sets, two questions need to be answered: (i) how to select an AS approach and (ii) how to set its parameters effectively. We address both of these problems simultaneously by using automated algorithm configuration. Specifically, we demonstrate that we can use algorithm configurators to automatically configure claspfolio 2, which implements a large variety of different AS approaches and their respective parameters in a single highly parameterized algorithm framework. We demonstrate that this approach, dubbed Auto Folio, can significantly improve the performance of claspfolio 2 on 11 out of the 12 scenarios from the Algorithm Selection Library and leads to new state-of-the-art algorithm selectors for 9 of these scenarios.
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Algorithm Configuration: papers presented at the Twenty-Ninth AAAI Conference on Artificial Intelligence. AI Access Foundation, 2015. S. 9-15 (AAAI Workshop - Technical Report).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Auto folio: Algorithm configuration for algorithm selection
AU - Lindauer, Marius
AU - Hoos, Holger H.
AU - Schaub, Torsten
AU - Hutter, Frank
PY - 2015
Y1 - 2015
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 QBE 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. This holds specifically for the machine learning techniques that form the core of current AS procedures and for their hyperparameters. Therefore, to successfully apply AS to new problems, algorithms and benchmark sets, two questions need to be answered: (i) how to select an AS approach and (ii) how to set its parameters effectively. We address both of these problems simultaneously by using automated algorithm configuration. Specifically, we demonstrate that we can use algorithm configurators to automatically configure claspfolio 2, which implements a large variety of different AS approaches and their respective parameters in a single highly parameterized algorithm framework. We demonstrate that this approach, dubbed Auto Folio, can significantly improve the performance of claspfolio 2 on 11 out of the 12 scenarios from the Algorithm Selection Library and leads to new state-of-the-art algorithm selectors for 9 of these scenarios.
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 QBE 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. This holds specifically for the machine learning techniques that form the core of current AS procedures and for their hyperparameters. Therefore, to successfully apply AS to new problems, algorithms and benchmark sets, two questions need to be answered: (i) how to select an AS approach and (ii) how to set its parameters effectively. We address both of these problems simultaneously by using automated algorithm configuration. Specifically, we demonstrate that we can use algorithm configurators to automatically configure claspfolio 2, which implements a large variety of different AS approaches and their respective parameters in a single highly parameterized algorithm framework. We demonstrate that this approach, dubbed Auto Folio, can significantly improve the performance of claspfolio 2 on 11 out of the 12 scenarios from the Algorithm Selection Library and leads to new state-of-the-art algorithm selectors for 9 of these scenarios.
UR - http://www.scopus.com/inward/record.url?scp=84964607371&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84964607371
T3 - AAAI Workshop - Technical Report
SP - 9
EP - 15
BT - Algorithm Configuration
PB - AI Access Foundation
T2 - 29th AAAI Conference on Artificial Intelligence, AAAI 2015
Y2 - 25 January 2015 through 30 January 2015
ER -