Constraint acquisition

Constraint programming is used to model and solve complex combinatorial problems. The modeling task requires some expertise in constraint programming. This requirement is a bottleneck to the broader uptake of constraint technology. Several approaches have been proposed to assist the non-expert user...

Full description

Saved in:
Bibliographic Details
Published in:Artificial intelligence Vol. 244; pp. 315 - 342
Main Authors: Bessiere, Christian, Koriche, Frédéric, Lazaar, Nadjib, O'Sullivan, Barry
Format: Journal Article
Language:English
Published: Amsterdam Elsevier B.V 01.03.2017
Elsevier Science Ltd
Elsevier
Subjects:
ISSN:0004-3702, 1872-7921
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Abstract Constraint programming is used to model and solve complex combinatorial problems. The modeling task requires some expertise in constraint programming. This requirement is a bottleneck to the broader uptake of constraint technology. Several approaches have been proposed to assist the non-expert user in the modeling task. This paper presents the basic architecture for acquiring constraint networks from examples classified by the user. The theoretical questions raised by constraint acquisition are stated and their complexity is given. We then propose Conacq, a system that uses a concise representation of the learner's version space into a clausal formula. Based on this logical representation, our architecture uses strategies for eliciting constraint networks in both the passive acquisition context, where the learner is only provided a pool of examples, and the active acquisition context, where the learner is allowed to ask membership queries to the user. The computational properties of our strategies are analyzed and their practical effectiveness is experimentally evaluated.
AbstractList Constraint programming is used to model and solve complex combinatorial problems. The modeling task requires some expertise in constraint programming. This requirement is a bottleneck to the broader uptake of constraint technology. Several approaches have been proposed to assist the non-expert user in the modeling task. This paper presents the basic architecture for acquiring constraint networks from examples classified by the user. The theoretical questions raised by constraint acquisition are stated and their complexity is given. We then propose Conacq, a system that uses a concise representation of the learner's version space into a clausal formula. Based on this logical representation, our architecture uses strategies for eliciting constraint networks in both the passive acquisition context, where the learner is only provided a pool of examples, and the active acquisition context, where the learner is allowed to ask membership queries to the user. The computational properties of our strategies are analyzed and their practical effectiveness is experimentally evaluated.
Constraint programming is used to model and solve complex combina- torial problems. The modeling task requires some expertise in constraint programming. This requirement is a bottleneck to the broader uptake of constraint technology. Several approaches have been proposed to assist the non-expert user in the modelling task. This paper presents the basic architecture for acquiring constraint networks from examples classified by the user. The theoretical questions raised by constraint acquisition are stated and their complexity is given. We then propose Conacq, a sys- tem that uses a concise representation of the learner’s version space into a clausal formula. Based on this logical representation, our architecture uses strategies for eliciting constraint networks in both the passive acquisition context, where the learner is only provided a pool of examples, and the active acquisition context, where the learner is allowed to ask membership queries to the user. The computational properties of our strategies are analyzed and their practical effectiveness is experimentally evaluated.
Author O'Sullivan, Barry
Lazaar, Nadjib
Bessiere, Christian
Koriche, Frédéric
Author_xml – sequence: 1
  givenname: Christian
  surname: Bessiere
  fullname: Bessiere, Christian
  email: bessiere@lirmm.fr
  organization: University of Montpellier, France
– sequence: 2
  givenname: Frédéric
  surname: Koriche
  fullname: Koriche, Frédéric
  organization: University of Artois, France
– sequence: 3
  givenname: Nadjib
  surname: Lazaar
  fullname: Lazaar, Nadjib
  organization: University of Montpellier, France
– sequence: 4
  givenname: Barry
  surname: O'Sullivan
  fullname: O'Sullivan, Barry
  organization: University College Cork, Ireland
BackLink https://hal-lirmm.ccsd.cnrs.fr/lirmm-01276188$$DView record in HAL
BookMark eNqFkMFLwzAUxoNMcJtePXkQPErre-napB6EMdQJAy96DmmaYkrXbEk28L83peLBg54eD77fx8dvRia97TUhVwgpAhZ3bSpdMH1IKWCeAk8B8IRMkTOasJLihEwBYJFkDOgZmXnfxjcrS5ySy5XtfXAy0tdS7Q_Gm2Bsf05OG9l5ffF95-T96fFttU42r88vq-UmUQssQpJRkFBWOtd1lVesoHnFcxqLZSMxQ6A5o5hBIbViqHncWkvWFMg5Z7UuZTYnt2Pvh-zEzpmtdJ_CSiPWy43ojNtuBSBlA3HEmL4Z0ztn9wftg2jtwfVxoMASi6xAhnlMLcaUctZ7p5ufYgQx-BKtGH2JwZcALqKviN3_wpQJcpAx6On-gx9GWEdZR6Od8MroXunaOK2CqK35u-ALg52H_w
CitedBy_id crossref_primary_10_1007_s10601_023_09363_2
crossref_primary_10_1016_j_ijar_2024_109206
crossref_primary_10_3233_AAC_190471
crossref_primary_10_1080_03155986_2024_2381306
crossref_primary_10_1016_j_artint_2021_103599
crossref_primary_10_1287_ijoc_2018_0855
crossref_primary_10_1016_j_artint_2022_103822
crossref_primary_10_1109_TNET_2020_3045595
crossref_primary_10_1016_j_artint_2022_103751
crossref_primary_10_1016_j_artint_2023_103896
crossref_primary_10_1016_j_ecoinf_2022_101607
crossref_primary_10_1016_j_ifacol_2021_08_074
crossref_primary_10_1016_j_cor_2018_04_006
crossref_primary_10_1007_s10601_020_09311_4
crossref_primary_10_1016_j_artint_2024_104257
crossref_primary_10_1016_j_ijar_2024_109184
crossref_primary_10_1007_s10472_021_09736_4
crossref_primary_10_1007_s10601_017_9275_0
crossref_primary_10_1155_ijcg_8864472
crossref_primary_10_4271_12_08_01_0003
crossref_primary_10_1080_00207543_2022_2069525
Cites_doi 10.1016/j.tcs.2003.11.004
10.1016/0004-3702(82)90040-6
10.1016/j.artint.2003.04.003
10.1006/jcss.1996.0021
10.1016/0004-3702(88)90002-1
10.1007/s10601-006-9010-8
10.1145/1968.1972
10.1016/S0304-3975(97)86737-0
10.1007/978-3-540-68856-3
10.1006/jcss.1995.1075
10.1023/A:1007361123060
10.1038/22055
10.1016/0743-1066(84)90014-1
ContentType Journal Article
Copyright 2015 Elsevier B.V.
Copyright Elsevier Science Ltd. Mar 2017
licence_http://creativecommons.org/publicdomain/zero
Copyright_xml – notice: 2015 Elsevier B.V.
– notice: Copyright Elsevier Science Ltd. Mar 2017
– notice: licence_http://creativecommons.org/publicdomain/zero
DBID AAYXX
CITATION
7SC
8FD
JQ2
L7M
L~C
L~D
1XC
VOOES
DOI 10.1016/j.artint.2015.08.001
DatabaseName CrossRef
Computer and Information Systems Abstracts
Technology Research Database
ProQuest Computer Science Collection
Advanced Technologies Database with Aerospace
Computer and Information Systems Abstracts – Academic
Computer and Information Systems Abstracts Professional
Hyper Article en Ligne (HAL)
Hyper Article en Ligne (HAL) (Open Access)
DatabaseTitle CrossRef
Computer and Information Systems Abstracts
Technology Research Database
Computer and Information Systems Abstracts – Academic
Advanced Technologies Database with Aerospace
ProQuest Computer Science Collection
Computer and Information Systems Abstracts Professional
DatabaseTitleList Computer and Information Systems Abstracts


DeliveryMethod fulltext_linktorsrc
Discipline Computer Science
EISSN 1872-7921
EndPage 342
ExternalDocumentID oai:HAL:lirmm-01276188v1
10_1016_j_artint_2015_08_001
S0004370215001162
GroupedDBID --K
--M
--Z
-~X
.DC
.~1
0R~
1B1
1~.
1~5
23N
4.4
457
4G.
5GY
5VS
6I.
6J9
6TJ
7-5
71M
77K
8P~
9JN
AACTN
AAEDT
AAEDW
AAFTH
AAIAV
AAIKJ
AAKOC
AAKPC
AALRI
AAOAW
AAQFI
AAQXK
AAXUO
AAYFN
ABBOA
ABFNM
ABFRF
ABJNI
ABMAC
ABVKL
ABXDB
ABYKQ
ACDAQ
ACGFO
ACGFS
ACNCT
ACNNM
ACRLP
ACWUS
ACZNC
ADBBV
ADEZE
ADMUD
AEBSH
AECPX
AEFWE
AEKER
AENEX
AETEA
AEXQZ
AFKWA
AFTJW
AGHFR
AGUBO
AGYEJ
AHHHB
AHJVU
AHZHX
AIALX
AIEXJ
AIKHN
AITUG
AJBFU
AJOXV
ALMA_UNASSIGNED_HOLDINGS
AMFUW
AMRAJ
AOUOD
ASPBG
AVWKF
AXJTR
AZFZN
BJAXD
BKOJK
BLXMC
CS3
E3Z
EBS
EFJIC
EFLBG
EJD
EO8
EO9
EP2
EP3
F0J
F5P
FDB
FEDTE
FGOYB
FIRID
FNPLU
FYGXN
G-2
G-Q
G8K
GBLVA
GBOLZ
HLZ
HVGLF
HZ~
IHE
IXB
J1W
JJJVA
KOM
KQ8
LG9
LY7
M41
MO0
MVM
N9A
NCXOZ
O-L
O9-
OAUVE
OK1
OZT
P-8
P-9
P2P
PC.
PQQKQ
Q38
R2-
RIG
RNS
ROL
RPZ
SBC
SDF
SDG
SDP
SES
SET
SEW
SPC
SPCBC
SST
SSV
SSZ
T5K
TAE
TN5
TR2
TWZ
UPT
UQL
VQA
WH7
WUQ
XFK
XJE
XJT
XPP
XSW
ZMT
~02
~G-
77I
9DU
AATTM
AAXKI
AAYWO
AAYXX
ABDPE
ABWVN
ACLOT
ACRPL
ACVFH
ADCNI
ADNMO
ADVLN
AEIPS
AEUPX
AFJKZ
AFPUW
AGQPQ
AIGII
AIIUN
AKBMS
AKRWK
AKYEP
ANKPU
APXCP
CITATION
EFKBS
~HD
7SC
8FD
AFXIZ
AGCQF
AGRNS
JQ2
L7M
L~C
L~D
SSH
1XC
VOOES
ID FETCH-LOGICAL-c416t-320a09be5edb5b7625b852991afa131025721306aec71e8016da7f618887de9a3
ISICitedReferencesCount 63
ISICitedReferencesURI http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000394630400016&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN 0004-3702
IngestDate Tue Nov 25 06:21:16 EST 2025
Fri Jul 25 02:28:45 EDT 2025
Tue Nov 18 21:45:30 EST 2025
Sat Nov 29 07:31:10 EST 2025
Fri Feb 23 02:31:59 EST 2024
IsDoiOpenAccess true
IsOpenAccess true
IsPeerReviewed true
IsScholarly true
Keywords Constraint learning
Constraint programming
Language English
License licence_http://creativecommons.org/publicdomain/zero/: http://creativecommons.org/publicdomain/zero
LinkModel OpenURL
MergedId FETCHMERGED-LOGICAL-c416t-320a09be5edb5b7625b852991afa131025721306aec71e8016da7f618887de9a3
Notes ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ORCID 0000-0003-4059-6403
0000-0002-6952-5775
0000-0003-2524-9462
OpenAccessLink https://hal-lirmm.ccsd.cnrs.fr/lirmm-01276188
PQID 1916361715
PQPubID 2038285
PageCount 28
ParticipantIDs hal_primary_oai_HAL_lirmm_01276188v1
proquest_journals_1916361715
crossref_primary_10_1016_j_artint_2015_08_001
crossref_citationtrail_10_1016_j_artint_2015_08_001
elsevier_sciencedirect_doi_10_1016_j_artint_2015_08_001
PublicationCentury 2000
PublicationDate March 2017
2017-03-00
20170301
2017-03
PublicationDateYYYYMMDD 2017-03-01
PublicationDate_xml – month: 03
  year: 2017
  text: March 2017
PublicationDecade 2010
PublicationPlace Amsterdam
PublicationPlace_xml – name: Amsterdam
PublicationTitle Artificial intelligence
PublicationYear 2017
Publisher Elsevier B.V
Elsevier Science Ltd
Elsevier
Publisher_xml – name: Elsevier B.V
– name: Elsevier Science Ltd
– name: Elsevier
References Smith, Rosenbloom (br0350) 1990
Bessiere, Coletta, Koriche, O'Sullivan (br0070) 2005; vol. 3720
Bessiere, Coletta, O'Sullivan, Paulin (br0090) 2007
Dowling, Gallier (br0170) 1984; 1
Gent, Walsh (br0210) 1999
Beldiceanu, Carlsson, Demassey, Petit (br0030) 2007; 12
Blum, Rudich (br0110) 1995; 51
Dechter (br0150) 2003
Leo, Mears, Tack, Garcia de la Banda (br0260) 2013; vol. 8124
Apt (br0020) 2003
Hirsh, Mishra, Pitt (br0240) 2004; 156
Mitchell (br0270) 1982; 18
De Readt, Dehaspe (br0310) 1997; 26
Angluin (br0010) 2004; 313
Dechter, van Beek (br0160) 1997; 173
Flach (br0180) 2012
Bshouty, Goldman, Hancock, Matar (br0120) 1996; 52
Frisch, Jefferson, Martínez Hernández, Miguel (br0200) 2005
Bessiere, Coletta, Hebrard, Katsirelos, Lazaar, Narodytska, Quimper, Walsh (br0060) 2013
Beldiceanu, Simonis (br0040) 2012; vol. 7514
Lallouet, Lopez, Martin, Vrain (br0250) 2010
Valiant (br0360) 1984; 27
Monasson, Zecchina, Kirkpatrick, Selman, Ttroyansky (br0280) 1999; 400
Freuder (br0190) 1999
Haussler (br0230) 1988; 36
Gunter, Ngair, Panangaden, Subramanian (br0220) 1991
Paulin, Bessiere, Sallantin (br0290) 2008
Bessiere, Coletta, Petit (br0100) 2007
Bessiere, Coletta, Freuder, O'Sullivan (br0050) 2004
Bessiere, Coletta, Koriche, O'Sullivan (br0080) 2006
Cohen, Jeavons (br0140) 2003
De Raedt (br0300) 2008
Rossi, van Beek, Walsh (br0320) 2006
Smith (br0340) 2006
Büning, Lettman (br0130) 1999; vol. 48
Shchekotykhin, Friedrich (br0330) 2009
Freuder (10.1016/j.artint.2015.08.001_br0190) 1999
Smith (10.1016/j.artint.2015.08.001_br0350) 1990
Apt (10.1016/j.artint.2015.08.001_br0020) 2003
Dechter (10.1016/j.artint.2015.08.001_br0150) 2003
Bshouty (10.1016/j.artint.2015.08.001_br0120) 1996; 52
Cohen (10.1016/j.artint.2015.08.001_br0140) 2003
Gent (10.1016/j.artint.2015.08.001_br0210)
Monasson (10.1016/j.artint.2015.08.001_br0280) 1999; 400
Smith (10.1016/j.artint.2015.08.001_br0340) 2006
Bessiere (10.1016/j.artint.2015.08.001_br0060) 2013
Frisch (10.1016/j.artint.2015.08.001_br0200) 2005
De Readt (10.1016/j.artint.2015.08.001_br0310) 1997; 26
Mitchell (10.1016/j.artint.2015.08.001_br0270) 1982; 18
Bessiere (10.1016/j.artint.2015.08.001_br0070) 2005; vol. 3720
Flach (10.1016/j.artint.2015.08.001_br0180) 2012
Büning (10.1016/j.artint.2015.08.001_br0130) 1999; vol. 48
De Raedt (10.1016/j.artint.2015.08.001_br0300) 2008
Beldiceanu (10.1016/j.artint.2015.08.001_br0040) 2012; vol. 7514
Gunter (10.1016/j.artint.2015.08.001_br0220) 1991
Blum (10.1016/j.artint.2015.08.001_br0110) 1995; 51
Lallouet (10.1016/j.artint.2015.08.001_br0250) 2010
Rossi (10.1016/j.artint.2015.08.001_br0320) 2006
Angluin (10.1016/j.artint.2015.08.001_br0010) 2004; 313
Paulin (10.1016/j.artint.2015.08.001_br0290) 2008
Bessiere (10.1016/j.artint.2015.08.001_br0100) 2007
Haussler (10.1016/j.artint.2015.08.001_br0230) 1988; 36
Bessiere (10.1016/j.artint.2015.08.001_br0050) 2004
Valiant (10.1016/j.artint.2015.08.001_br0360) 1984; 27
Bessiere (10.1016/j.artint.2015.08.001_br0090) 2007
Bessiere (10.1016/j.artint.2015.08.001_br0080) 2006
Beldiceanu (10.1016/j.artint.2015.08.001_br0030) 2007; 12
Dechter (10.1016/j.artint.2015.08.001_br0160) 1997; 173
Dowling (10.1016/j.artint.2015.08.001_br0170) 1984; 1
Leo (10.1016/j.artint.2015.08.001_br0260) 2013; vol. 8124
Shchekotykhin (10.1016/j.artint.2015.08.001_br0330) 2009
Hirsh (10.1016/j.artint.2015.08.001_br0240) 2004; 156
References_xml – volume: 1
  start-page: 267
  year: 1984
  end-page: 284
  ident: br0170
  article-title: Linear-time algorithms for testing the satisfiability of propositional horn formulae
  publication-title: J. Log. Program.
– year: 2006
  ident: br0320
  article-title: Handbook of Constraint Programming
  publication-title: Foundations of Artificial Intelligence
– volume: 12
  start-page: 21
  year: 2007
  end-page: 62
  ident: br0030
  article-title: Global constraint catalogue: past, present and future
  publication-title: Constraints
– start-page: 475
  year: 2013
  end-page: 481
  ident: br0060
  article-title: Constraint acquisition via partial queries
  publication-title: Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence
– start-page: 15
  year: 1999
  end-page: 21
  ident: br0190
  article-title: Modeling: the final frontier
  publication-title: 1st International Conference on the Practical Applications of Constraint Technologies and Logic Programming
– volume: 27
  start-page: 1134
  year: 1984
  end-page: 1142
  ident: br0360
  article-title: A theory of the learnable
  publication-title: Commun. ACM
– volume: 51
  start-page: 367
  year: 1995
  end-page: 373
  ident: br0110
  article-title: Fast learning of k-term dnf formulas with queries
  publication-title: J. Comput. Syst. Sci.
– year: 1999
  ident: br0210
  article-title: Csplib: a benchmark library for constraints
– volume: 36
  start-page: 177
  year: 1988
  end-page: 221
  ident: br0230
  article-title: Quantifying inductive bias: AI learning algorithms and Valiant's learning framework
  publication-title: Artif. Intell.
– volume: vol. 3720
  start-page: 23
  year: 2005
  end-page: 34
  ident: br0070
  article-title: A SAT-based version space algorithm for acquiring constraint satisfaction problems
  publication-title: Proceedings of the European Conference on Machine Learning
– volume: 400
  start-page: 133
  year: 1999
  end-page: 137
  ident: br0280
  article-title: Determining computational complexity from characteristic ‘phase transition’
  publication-title: Nature
– volume: vol. 7514
  start-page: 141
  year: 2012
  end-page: 157
  ident: br0040
  article-title: A model seeker: extracting global constraint models from positive examples
  publication-title: Proceedings of the Seventeenth International Conference on Principles and Practice of Constraint Programming
– volume: vol. 48
  year: 1999
  ident: br0130
  article-title: Propositional Logic: Decution and Algorithms
  publication-title: Cambridge Tracts in Theoretical Computer Science
– volume: 156
  start-page: 115
  year: 2004
  end-page: 138
  ident: br0240
  article-title: Version spaces and the consistency problem
  publication-title: Artif. Intell.
– start-page: 476
  year: 2009
  end-page: 482
  ident: br0330
  article-title: Argumentation based constraint acquisition
  publication-title: Proceedings of the Ninth IEEE International Conference on Data Mining
– volume: 18
  start-page: 203
  year: 1982
  end-page: 226
  ident: br0270
  article-title: Generalization as search
  publication-title: Artif. Intell.
– start-page: 109
  year: 2005
  end-page: 116
  ident: br0200
  article-title: The rules of constraint modeling
  publication-title: 19th International Joint Conference on Artificial Intelligence
– volume: 26
  start-page: 99
  year: 1997
  end-page: 146
  ident: br0310
  article-title: Clasual discovery
  publication-title: Mach. Learn.
– volume: 173
  start-page: 283
  year: 1997
  end-page: 308
  ident: br0160
  article-title: Local and global relational consistency
  publication-title: Theor. Comput. Sci.
– start-page: 123
  year: 2004
  end-page: 137
  ident: br0050
  article-title: Leveraging the learning power of examples in automated constraint acquisition
  publication-title: Proceedings of CP'04
– year: 2003
  ident: br0150
  article-title: Constraint Processing
– year: 2008
  ident: br0300
  article-title: Logical and Relational Learning
  publication-title: Cognitive Technologies
– year: 2006
  ident: br0340
  article-title: Modelling
  publication-title: Handbook of Constraint Programming
– volume: 313
  start-page: 175
  year: 2004
  end-page: 194
  ident: br0010
  article-title: Queries revisited
  publication-title: Theor. Comput. Sci.
– start-page: 275
  year: 2008
  end-page: 282
  ident: br0290
  article-title: Automatic design of robot behaviors through constraint network acquisition
  publication-title: Proceedings of the 20th IEEE International Conference on Tools for Artificial Intelligence
– start-page: 50
  year: 2007
  end-page: 55
  ident: br0100
  article-title: Learning implied global constraints
  publication-title: Proceedings IJCAI'07
– start-page: 500
  year: 1991
  end-page: 505
  ident: br0220
  article-title: The common order-theoretic structure of version spaces and atms's
  publication-title: 9th National Conference on Artificial Intelligence
– year: 2003
  ident: br0020
  article-title: Principles of Constraint Programming
– start-page: 45
  year: 2010
  end-page: 52
  ident: br0250
  article-title: On learning constraint problems
  publication-title: Proceedings of the 22nd IEEE International Conference on Tools for Artificial Intelligence
– year: 2003
  ident: br0140
  article-title: Tractable constraint languages
  publication-title: Constraint Processing
– year: 2012
  ident: br0180
  article-title: Machine Learning: The Art and Science of Algorithms That Make Sense of Data
– volume: vol. 8124
  start-page: 432
  year: 2013
  end-page: 447
  ident: br0260
  article-title: Globalizing constraint models
  publication-title: Proceedings of the Eighteenth International Conference on Principles and Practice of Constraint Programming
– volume: 52
  start-page: 268
  year: 1996
  end-page: 286
  ident: br0120
  article-title: Asking questions to minimize errors
  publication-title: J. Comput. Syst. Sci.
– start-page: 1565
  year: 2006
  end-page: 1568
  ident: br0080
  article-title: Acquiring constraint networks using a SAT-based version space algorithm
  publication-title: Proceedings AAAI'06
– start-page: 44
  year: 2007
  end-page: 49
  ident: br0090
  article-title: Query-driven constraint acquisition
  publication-title: Proceedings IJCAI'07
– start-page: 848
  year: 1990
  end-page: 853
  ident: br0350
  article-title: Incremental non-backtracking focusing: a polynomially bounded generalization algorithm for version spaces
  publication-title: Proceedings of the 8th National Conference on Artificial Intelligence
– volume: 313
  start-page: 175
  year: 2004
  ident: 10.1016/j.artint.2015.08.001_br0010
  article-title: Queries revisited
  publication-title: Theor. Comput. Sci.
  doi: 10.1016/j.tcs.2003.11.004
– start-page: 1565
  year: 2006
  ident: 10.1016/j.artint.2015.08.001_br0080
  article-title: Acquiring constraint networks using a SAT-based version space algorithm
– start-page: 475
  year: 2013
  ident: 10.1016/j.artint.2015.08.001_br0060
  article-title: Constraint acquisition via partial queries
– ident: 10.1016/j.artint.2015.08.001_br0210
– volume: 18
  start-page: 203
  issue: 2
  year: 1982
  ident: 10.1016/j.artint.2015.08.001_br0270
  article-title: Generalization as search
  publication-title: Artif. Intell.
  doi: 10.1016/0004-3702(82)90040-6
– year: 2003
  ident: 10.1016/j.artint.2015.08.001_br0140
  article-title: Tractable constraint languages
– volume: 156
  start-page: 115
  issue: 2
  year: 2004
  ident: 10.1016/j.artint.2015.08.001_br0240
  article-title: Version spaces and the consistency problem
  publication-title: Artif. Intell.
  doi: 10.1016/j.artint.2003.04.003
– volume: vol. 7514
  start-page: 141
  year: 2012
  ident: 10.1016/j.artint.2015.08.001_br0040
  article-title: A model seeker: extracting global constraint models from positive examples
– volume: vol. 48
  year: 1999
  ident: 10.1016/j.artint.2015.08.001_br0130
  article-title: Propositional Logic: Decution and Algorithms
– start-page: 15
  year: 1999
  ident: 10.1016/j.artint.2015.08.001_br0190
  article-title: Modeling: the final frontier
– year: 2006
  ident: 10.1016/j.artint.2015.08.001_br0340
  article-title: Modelling
– start-page: 476
  year: 2009
  ident: 10.1016/j.artint.2015.08.001_br0330
  article-title: Argumentation based constraint acquisition
– year: 2003
  ident: 10.1016/j.artint.2015.08.001_br0020
– volume: 52
  start-page: 268
  issue: 2
  year: 1996
  ident: 10.1016/j.artint.2015.08.001_br0120
  article-title: Asking questions to minimize errors
  publication-title: J. Comput. Syst. Sci.
  doi: 10.1006/jcss.1996.0021
– start-page: 275
  year: 2008
  ident: 10.1016/j.artint.2015.08.001_br0290
  article-title: Automatic design of robot behaviors through constraint network acquisition
– volume: 36
  start-page: 177
  year: 1988
  ident: 10.1016/j.artint.2015.08.001_br0230
  article-title: Quantifying inductive bias: AI learning algorithms and Valiant's learning framework
  publication-title: Artif. Intell.
  doi: 10.1016/0004-3702(88)90002-1
– volume: 12
  start-page: 21
  issue: 1
  year: 2007
  ident: 10.1016/j.artint.2015.08.001_br0030
  article-title: Global constraint catalogue: past, present and future
  publication-title: Constraints
  doi: 10.1007/s10601-006-9010-8
– start-page: 50
  year: 2007
  ident: 10.1016/j.artint.2015.08.001_br0100
  article-title: Learning implied global constraints
– volume: 27
  start-page: 1134
  issue: 11
  year: 1984
  ident: 10.1016/j.artint.2015.08.001_br0360
  article-title: A theory of the learnable
  publication-title: Commun. ACM
  doi: 10.1145/1968.1972
– start-page: 44
  year: 2007
  ident: 10.1016/j.artint.2015.08.001_br0090
  article-title: Query-driven constraint acquisition
– year: 2006
  ident: 10.1016/j.artint.2015.08.001_br0320
  article-title: Handbook of Constraint Programming
– volume: 173
  start-page: 283
  issue: 1
  year: 1997
  ident: 10.1016/j.artint.2015.08.001_br0160
  article-title: Local and global relational consistency
  publication-title: Theor. Comput. Sci.
  doi: 10.1016/S0304-3975(97)86737-0
– year: 2008
  ident: 10.1016/j.artint.2015.08.001_br0300
  article-title: Logical and Relational Learning
  doi: 10.1007/978-3-540-68856-3
– start-page: 500
  year: 1991
  ident: 10.1016/j.artint.2015.08.001_br0220
  article-title: The common order-theoretic structure of version spaces and atms's
– year: 2012
  ident: 10.1016/j.artint.2015.08.001_br0180
– volume: 51
  start-page: 367
  issue: 3
  year: 1995
  ident: 10.1016/j.artint.2015.08.001_br0110
  article-title: Fast learning of k-term dnf formulas with queries
  publication-title: J. Comput. Syst. Sci.
  doi: 10.1006/jcss.1995.1075
– volume: 26
  start-page: 99
  year: 1997
  ident: 10.1016/j.artint.2015.08.001_br0310
  article-title: Clasual discovery
  publication-title: Mach. Learn.
  doi: 10.1023/A:1007361123060
– start-page: 123
  year: 2004
  ident: 10.1016/j.artint.2015.08.001_br0050
  article-title: Leveraging the learning power of examples in automated constraint acquisition
– start-page: 45
  year: 2010
  ident: 10.1016/j.artint.2015.08.001_br0250
  article-title: On learning constraint problems
– volume: 400
  start-page: 133
  year: 1999
  ident: 10.1016/j.artint.2015.08.001_br0280
  article-title: Determining computational complexity from characteristic ‘phase transition’
  publication-title: Nature
  doi: 10.1038/22055
– start-page: 848
  year: 1990
  ident: 10.1016/j.artint.2015.08.001_br0350
  article-title: Incremental non-backtracking focusing: a polynomially bounded generalization algorithm for version spaces
– volume: vol. 3720
  start-page: 23
  year: 2005
  ident: 10.1016/j.artint.2015.08.001_br0070
  article-title: A SAT-based version space algorithm for acquiring constraint satisfaction problems
– year: 2003
  ident: 10.1016/j.artint.2015.08.001_br0150
– volume: 1
  start-page: 267
  issue: 3
  year: 1984
  ident: 10.1016/j.artint.2015.08.001_br0170
  article-title: Linear-time algorithms for testing the satisfiability of propositional horn formulae
  publication-title: J. Log. Program.
  doi: 10.1016/0743-1066(84)90014-1
– volume: vol. 8124
  start-page: 432
  year: 2013
  ident: 10.1016/j.artint.2015.08.001_br0260
  article-title: Globalizing constraint models
– start-page: 109
  year: 2005
  ident: 10.1016/j.artint.2015.08.001_br0200
  article-title: The rules of constraint modeling
SSID ssj0003991
Score 2.5154977
Snippet Constraint programming is used to model and solve complex combinatorial problems. The modeling task requires some expertise in constraint programming. This...
Constraint programming is used to model and solve complex combina- torial problems. The modeling task requires some expertise in constraint programming. This...
SourceID hal
proquest
crossref
elsevier
SourceType Open Access Repository
Aggregation Database
Enrichment Source
Index Database
Publisher
StartPage 315
SubjectTerms Artificial Intelligence
Combinatorial analysis
Complexity
Computer architecture
Computer Science
Constraint learning
Constraint programming
Theory of constraints
Title Constraint acquisition
URI https://dx.doi.org/10.1016/j.artint.2015.08.001
https://www.proquest.com/docview/1916361715
https://hal-lirmm.ccsd.cnrs.fr/lirmm-01276188
Volume 244
WOSCitedRecordID wos000394630400016&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
journalDatabaseRights – providerCode: PRVESC
  databaseName: Elsevier SD Freedom Collection Journals 2021
  customDbUrl:
  eissn: 1872-7921
  dateEnd: 20180131
  omitProxy: false
  ssIdentifier: ssj0003991
  issn: 0004-3702
  databaseCode: AIEXJ
  dateStart: 19950101
  isFulltext: true
  titleUrlDefault: https://www.sciencedirect.com
  providerName: Elsevier
link http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV07T8MwELaAMrDwRpSXOrChoDqJm2QsCMRLFQNI3Sy_IlLRUNKCEL-ec-yYQMVrYEmrKHHs3PnuO-fzHUL7CQc3DoGHF3YYBChSCo_LGHuckVQSgaXgpthE1OvF_X5ybfnz47KcQJTn8ctLMvpXUcM5ELbeOvsHcbtG4QT8B6HDEcQOx18JXpfgLAs_TA6YeHzKDCmrDkK7RUkQKst11DJyushcM2OVWZs2uQdqKnSpc4rcGU6w_couzU-RCcfuYa_MELd7TA4yXmfW1L8-HbHCspDtugP4Mke8crY0BPPU_mBL_TCsWcPA7NS0jjUwabSmbLZZPhgclmkTNL0VkzKrqn3YhxTZn1yXIxRWXLUBNa1Q3QrVBTb13r6GH5EETF6je37Sv3COGrCZLahoBlLtrCzpf9O9-Qq5zN5pCu0nT17Ck5tltGjjilbX6MMKmlH5Klqqana0rAlfQxvv6tGqqcc6uj09uTk-82xtDE8AhJ54gd9m7YQroiQnHDwagUkH0AKzlGGA7GCJfYAnHaZEhBXAkI5kUdrBMTgVqRIWbKC5_CFXm6glAVNzFaQkTeOQx5ynyvehFT_gUVsq3ERBNW4qbOJ43c97-t1bbyLP3TUyiVN-uD6qXim14M-AOgp68sOd-yAB9xCdL_2se0Xvs2I4pJpZoUf9DJftVCKidrqOKYboKAAQj8nWH_u7jRbeZ8UOmpsUT2oXzYvnSTYu9qyqvQHvv5BK
linkProvider Elsevier
openUrl ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Constraint+acquisition&rft.jtitle=Artificial+intelligence&rft.au=Bessiere%2C+Christian&rft.au=Koriche%2C+Fr%C3%A9d%C3%A9ric&rft.au=Lazaar%2C+Nadjib&rft.au=O%27Sullivan%2C+Barry&rft.date=2017-03-01&rft.issn=0004-3702&rft.volume=244&rft.spage=315&rft.epage=342&rft_id=info:doi/10.1016%2Fj.artint.2015.08.001&rft.externalDBID=n%2Fa&rft.externalDocID=10_1016_j_artint_2015_08_001
thumbnail_l http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0004-3702&client=summon
thumbnail_m http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0004-3702&client=summon
thumbnail_s http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0004-3702&client=summon