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Learning when to use lazy learning in constraint solving

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Learning when to use lazy learning in constraint solving. / Gent, Ian P.; Jefferson, Chris; Kotthoff, Lars; Miguel, Ian; Moore, Neil C. A.; Nightingale, Peter; Petrie, Karen.

ECAI 2010: 19th European conference on artificial intelligence, 16-20 August 2010, Lisbon, Portugal - including Prestigious applications of artificial intelligence (PAIS-2010) proceedings. ed. / Helder Coelho; Rudi Studer; Michael Wooldridge. Amsterdam : IOS Press, 2010. p. 873-878 (Frontiers in artificial intelligence and applications).

Research output: Chapter in Book/Report/Conference proceedingOther chapter contribution

Harvard

Gent, IP, Jefferson, C, Kotthoff, L, Miguel, I, Moore, NCA, Nightingale, P & Petrie, K 2010, 'Learning when to use lazy learning in constraint solving'. in H Coelho, R Studer & M Wooldridge (eds), ECAI 2010: 19th European conference on artificial intelligence, 16-20 August 2010, Lisbon, Portugal - including Prestigious applications of artificial intelligence (PAIS-2010) proceedings. Frontiers in artificial intelligence and applications, vol. 215, IOS Press, Amsterdam, pp. 873-878.

APA

Gent, I. P., Jefferson, C., Kotthoff, L., Miguel, I., Moore, N. C. A., Nightingale, P., & Petrie, K. (2010). Learning when to use lazy learning in constraint solving. In Coelho, H., Studer, R., & Wooldridge, M. (Eds.), ECAI 2010. (pp. 873-878). (Frontiers in artificial intelligence and applications). Amsterdam: IOS Press. doi: 10.3233/978-1-60750-606-5-873

Vancouver

Gent IP, Jefferson C, Kotthoff L, Miguel I, Moore NCA, Nightingale P et al. Learning when to use lazy learning in constraint solving. In Coelho H, Studer R, Wooldridge M, editors, ECAI 2010: 19th European conference on artificial intelligence, 16-20 August 2010, Lisbon, Portugal - including Prestigious applications of artificial intelligence (PAIS-2010) proceedings. Amsterdam: IOS Press. 2010. p. 873-878. (Frontiers in artificial intelligence and applications).

Author

Gent, Ian P.; Jefferson, Chris; Kotthoff, Lars; Miguel, Ian; Moore, Neil C. A.; Nightingale, Peter; Petrie, Karen / Learning when to use lazy learning in constraint solving.

ECAI 2010: 19th European conference on artificial intelligence, 16-20 August 2010, Lisbon, Portugal - including Prestigious applications of artificial intelligence (PAIS-2010) proceedings. ed. / Helder Coelho; Rudi Studer; Michael Wooldridge. Amsterdam : IOS Press, 2010. p. 873-878 (Frontiers in artificial intelligence and applications).

Research output: Chapter in Book/Report/Conference proceedingOther chapter contribution

Bibtex - Download

@inbook{9f77e96c968a41d984e3c619835fb768,
title = "Learning when to use lazy learning in constraint solving",
publisher = "IOS Press",
author = "Gent, {Ian P.} and Chris Jefferson and Lars Kotthoff and Ian Miguel and Moore, {Neil C. A.} and Peter Nightingale and Karen Petrie",
year = "2010",
editor = "Helder Coelho and Rudi Studer and Michael Wooldridge",
isbn = "9781607506058",
series = "Frontiers in artificial intelligence and applications",
pages = "873-878",
booktitle = "ECAI 2010",

}

RIS (suitable for import to EndNote) - Download

TY - CHAP

T1 - Learning when to use lazy learning in constraint solving

A1 - Gent,Ian P.

A1 - Jefferson,Chris

A1 - Kotthoff,Lars

A1 - Miguel,Ian

A1 - Moore,Neil C. A.

A1 - Nightingale,Peter

A1 - Petrie,Karen

AU - Gent,Ian P.

AU - Jefferson,Chris

AU - Kotthoff,Lars

AU - Miguel,Ian

AU - Moore,Neil C. A.

AU - Nightingale,Peter

AU - Petrie,Karen

PB - IOS Press

CY - Amsterdam

PY - 2010

Y1 - 2010

N2 - Learning in the context of constraint solving is a technique by which previously unknown constraints are uncovered during search and used to speed up subsequent search. Recently, lazy learning, similar to a successful idea from satisfiability modulo theories solvers, has been shown to be an effective means of incorporating constraint learning into a solver. Although a powerful technique to reduce search in some circumstances, lazy learning introduces a substantial overhead, which can outweigh its benefits. Hence, it is desirable to know beforehand whether or not it is expected to be useful. We approach this problem using machine learning (ML). We show that, in the context of a large benchmark set, standard ML approaches can be used to learn a simple, cheap classifier which performs well in identifying instances on which lazy learning should or should not be used. Furthermore, we demonstrate significant performance improvements of a system using our classifier and the lazy learning and standard constraint solvers over a standard solver. Through rigorous cross-validation across the different problem classes in our benchmark set, we show the general applicability of our learned classifier. © 2010 The authors and IOS Press. All rights reserved.

AB - Learning in the context of constraint solving is a technique by which previously unknown constraints are uncovered during search and used to speed up subsequent search. Recently, lazy learning, similar to a successful idea from satisfiability modulo theories solvers, has been shown to be an effective means of incorporating constraint learning into a solver. Although a powerful technique to reduce search in some circumstances, lazy learning introduces a substantial overhead, which can outweigh its benefits. Hence, it is desirable to know beforehand whether or not it is expected to be useful. We approach this problem using machine learning (ML). We show that, in the context of a large benchmark set, standard ML approaches can be used to learn a simple, cheap classifier which performs well in identifying instances on which lazy learning should or should not be used. Furthermore, we demonstrate significant performance improvements of a system using our classifier and the lazy learning and standard constraint solvers over a standard solver. Through rigorous cross-validation across the different problem classes in our benchmark set, we show the general applicability of our learned classifier. © 2010 The authors and IOS Press. All rights reserved.

UR - http://www.scopus.com/inward/record.url?partnerID=yv4JPVwI&eid=2-s2.0-77956051101&md5=80ab99f75715abdcae5348765788d509

U2 - 10.3233/978-1-60750-606-5-873

DO - 10.3233/978-1-60750-606-5-873

M1 - Other chapter contribution

SN - 9781607506058

BT - ECAI 2010

T2 - ECAI 2010

A2 - Wooldridge,Michael

ED - Wooldridge,Michael

T3 - Frontiers in artificial intelligence and applications

T3 - en_GB

SP - 873

EP - 878

ER -

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