Aslib: a benchmark library for algorithm selection

B. Bischl, P. Kerschke, L. Kotthoff, M. Lindauer, Y. Malitsky, A. Fréchette, H. Hoos, F. Hutter, K. Leyton-Brown, K. Tierney, J. Vanschoren

Onderzoeksoutput: Bijdrage aan tijdschriftTijdschriftartikelAcademicpeer review

41 Citaties (Scopus)

Uittreksel

The task of algorithm selection involves choosing an algorithm from a set of algorithms on a per-instance basis in order to exploit the varying performance of algorithms over a set of instances. The algorithm selection problem is attracting increasing attention from researchers and practitioners in AI. Years of fruitful applications in a number of domains have resulted in a large amount of data, but the community lacks a standard format or repository for this data. This situation makes it difficult to share and compare different approaches effectively, as is done in other, more established fields. It also unnecessarily hinders new researchers who want to work in this area. To address this problem, we introduce a standardized format for representing algorithm selection scenarios and a repository that contains a growing number of data sets from the literature. Our format has been designed to be able to express a wide variety of different scenarios. To demonstrate the breadth and power of our platform, we describe a study that builds and evaluates algorithm selection models through a common interface. The results display the potential of algorithm selection to achieve significant performance improvements across a broad range of problems and algorithms.
TaalEngels
Pagina's41-58
Aantal pagina's18
TijdschriftArtificial Intelligence
Volume237
DOI's
StatusGepubliceerd - 2016

Vingerafdruk

scenario
Benchmark
artificial intelligence
Set theory
performance
lack
community
Scenarios
Repository
literature
Model Selection

Citeer dit

Bischl, B., Kerschke, P., Kotthoff, L., Lindauer, M., Malitsky, Y., Fréchette, A., ... Vanschoren, J. (2016). Aslib: a benchmark library for algorithm selection. Artificial Intelligence, 237, 41-58. DOI: 10.1016/j.artint.2016.04.003
Bischl, B. ; Kerschke, P. ; Kotthoff, L. ; Lindauer, M. ; Malitsky, Y. ; Fréchette, A. ; Hoos, H. ; Hutter, F. ; Leyton-Brown, K. ; Tierney, K. ; Vanschoren, J./ Aslib: a benchmark library for algorithm selection. In: Artificial Intelligence. 2016 ; Vol. 237. blz. 41-58
@article{2dd09d8055cc444884329d989c1dbf02,
title = "Aslib: a benchmark library for algorithm selection",
abstract = "The task of algorithm selection involves choosing an algorithm from a set of algorithms on a per-instance basis in order to exploit the varying performance of algorithms over a set of instances. The algorithm selection problem is attracting increasing attention from researchers and practitioners in AI. Years of fruitful applications in a number of domains have resulted in a large amount of data, but the community lacks a standard format or repository for this data. This situation makes it difficult to share and compare different approaches effectively, as is done in other, more established fields. It also unnecessarily hinders new researchers who want to work in this area. To address this problem, we introduce a standardized format for representing algorithm selection scenarios and a repository that contains a growing number of data sets from the literature. Our format has been designed to be able to express a wide variety of different scenarios. To demonstrate the breadth and power of our platform, we describe a study that builds and evaluates algorithm selection models through a common interface. The results display the potential of algorithm selection to achieve significant performance improvements across a broad range of problems and algorithms.",
author = "B. Bischl and P. Kerschke and L. Kotthoff and M. Lindauer and Y. Malitsky and A. Fr{\'e}chette and H. Hoos and F. Hutter and K. Leyton-Brown and K. Tierney and J. Vanschoren",
year = "2016",
doi = "10.1016/j.artint.2016.04.003",
language = "English",
volume = "237",
pages = "41--58",
journal = "Artificial Intelligence",
issn = "0004-3702",
publisher = "Agon Elsevier",

}

Bischl, B, Kerschke, P, Kotthoff, L, Lindauer, M, Malitsky, Y, Fréchette, A, Hoos, H, Hutter, F, Leyton-Brown, K, Tierney, K & Vanschoren, J 2016, 'Aslib: a benchmark library for algorithm selection' Artificial Intelligence, vol. 237, blz. 41-58. DOI: 10.1016/j.artint.2016.04.003

Aslib: a benchmark library for algorithm selection. / Bischl, B.; Kerschke, P.; Kotthoff, L.; Lindauer, M.; Malitsky, Y.; Fréchette, A.; Hoos, H.; Hutter, F.; Leyton-Brown, K.; Tierney, K.; Vanschoren, J.

In: Artificial Intelligence, Vol. 237, 2016, blz. 41-58.

Onderzoeksoutput: Bijdrage aan tijdschriftTijdschriftartikelAcademicpeer review

TY - JOUR

T1 - Aslib: a benchmark library for algorithm selection

AU - Bischl,B.

AU - Kerschke,P.

AU - Kotthoff,L.

AU - Lindauer,M.

AU - Malitsky,Y.

AU - Fréchette,A.

AU - Hoos,H.

AU - Hutter,F.

AU - Leyton-Brown,K.

AU - Tierney,K.

AU - Vanschoren,J.

PY - 2016

Y1 - 2016

N2 - The task of algorithm selection involves choosing an algorithm from a set of algorithms on a per-instance basis in order to exploit the varying performance of algorithms over a set of instances. The algorithm selection problem is attracting increasing attention from researchers and practitioners in AI. Years of fruitful applications in a number of domains have resulted in a large amount of data, but the community lacks a standard format or repository for this data. This situation makes it difficult to share and compare different approaches effectively, as is done in other, more established fields. It also unnecessarily hinders new researchers who want to work in this area. To address this problem, we introduce a standardized format for representing algorithm selection scenarios and a repository that contains a growing number of data sets from the literature. Our format has been designed to be able to express a wide variety of different scenarios. To demonstrate the breadth and power of our platform, we describe a study that builds and evaluates algorithm selection models through a common interface. The results display the potential of algorithm selection to achieve significant performance improvements across a broad range of problems and algorithms.

AB - The task of algorithm selection involves choosing an algorithm from a set of algorithms on a per-instance basis in order to exploit the varying performance of algorithms over a set of instances. The algorithm selection problem is attracting increasing attention from researchers and practitioners in AI. Years of fruitful applications in a number of domains have resulted in a large amount of data, but the community lacks a standard format or repository for this data. This situation makes it difficult to share and compare different approaches effectively, as is done in other, more established fields. It also unnecessarily hinders new researchers who want to work in this area. To address this problem, we introduce a standardized format for representing algorithm selection scenarios and a repository that contains a growing number of data sets from the literature. Our format has been designed to be able to express a wide variety of different scenarios. To demonstrate the breadth and power of our platform, we describe a study that builds and evaluates algorithm selection models through a common interface. The results display the potential of algorithm selection to achieve significant performance improvements across a broad range of problems and algorithms.

U2 - 10.1016/j.artint.2016.04.003

DO - 10.1016/j.artint.2016.04.003

M3 - Article

VL - 237

SP - 41

EP - 58

JO - Artificial Intelligence

T2 - Artificial Intelligence

JF - Artificial Intelligence

SN - 0004-3702

ER -

Bischl B, Kerschke P, Kotthoff L, Lindauer M, Malitsky Y, Fréchette A et al. Aslib: a benchmark library for algorithm selection. Artificial Intelligence. 2016;237:41-58. Beschikbaar vanaf, DOI: 10.1016/j.artint.2016.04.003