A unifying framework for iterative approximate best-response algorithms for distributed constraint optimization problems

Distributed constraint optimization problems (DCOPs) are important in many areas of computer science and optimization. In a DCOP, each variable is controlled by one of many autonomous agents, who together have the joint goal of maximizing a global objective function. A wide variety of techniques hav...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	Knowledge engineering review Ročník 26; číslo 4; s. 411 - 444
Hlavní autoři:	Chapman, Archie C., Rogers, Alex, Jennings, Nicholas R., Leslie, David S.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Cambridge Cambridge University Press 01.12.2011
Témata:	Algorithms Approximation Communication Computer science Dynamic programming Game theory Games Iterative methods Mathematical analysis Mathematical models Optimization Optimization techniques Sensors Taxonomy Utility functions Variables
ISSN:	0269-8889, 1469-8005
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Abstract	Distributed constraint optimization problems (DCOPs) are important in many areas of computer science and optimization. In a DCOP, each variable is controlled by one of many autonomous agents, who together have the joint goal of maximizing a global objective function. A wide variety of techniques have been explored to solve such problems, and here we focus on one of the main families, namely iterative approximate best-response algorithms used as local search algorithms for DCOPs. We define these algorithms as those in which, at each iteration, agents communicate only the states of the variables under their control to their neighbours on the constraint graph, and that reason about their next state based on the messages received from their neighbours. These algorithms include the distributed stochastic algorithm and stochastic coordination algorithms, the maximum-gain messaging algorithms, the families of fictitious play and adaptive play algorithms, and algorithms that use regret-based heuristics. This family of algorithms is commonly employed in real-world systems, as they can be used in domains where communication is difficult or costly, where it is appropriate to trade timeliness off against optimality, or where hardware limitations render complete or more computationally intensive algorithms unusable. However, until now, no overarching framework has existed for analyzing this broad family of algorithms, resulting in similar and overlapping work being published independently in several different literatures. The main contribution of this paper, then, is the development of a unified analytical framework for studying such algorithms. This framework is built on our insight that when formulated as non-cooperative games, DCOPs form a subset of the class of potential games. This result allows us to prove convergence properties of iterative approximate best-response algorithms developed in the computer science literature using game-theoretic methods (which also shows that such algorithms can also be applied to the more general problem of finding Nash equilibria in potential games), and, conversely, also allows us to show that many game-theoretic algorithms can be used to solve DCOPs. By so doing, our framework can assist system designers by making the pros and cons of, and the synergies between, the various iterative approximate best-response DCOP algorithm components clear.
AbstractList	Distributed constraint optimization problems (DCOPs) are important in many areas of computer science and optimization. In a DCOP, each variable is controlled by one of many autonomous agents, who together have the joint goal of maximizing a global objective function. A wide variety of techniques have been explored to solve such problems, and here we focus on one of the main families, namely iterative approximate best-response algorithms used as local search algorithms for DCOPs. We define these algorithms as those in which, at each iteration, agents communicate only the states of the variables under their control to their neighbours on the constraint graph, and that reason about their next state based on the messages received from their neighbours. These algorithms include the distributed stochastic algorithm and stochastic coordination algorithms, the maximum-gain messaging algorithms, the families of fictitious play and adaptive play algorithms, and algorithms that use regret-based heuristics. This family of algorithms is commonly employed in real-world systems, as they can be used in domains where communication is difficult or costly, where it is appropriate to trade timeliness off against optimality, or where hardware limitations render complete or more computationally intensive algorithms unusable. However, until now, no overarching framework has existed for analyzing this broad family of algorithms, resulting in similar and overlapping work being published independently in several different literatures. The main contribution of this paper, then, is the development of a unified analytical framework for studying such algorithms. This framework is built on our insight that when formulated as non-cooperative games, DCOPs form a subset of the class of potential games. This result allows us to prove convergence properties of iterative approximate best-response algorithms developed in the computer science literature using game-theoretic methods (which also shows that such algorithms can also be applied to the more general problem of finding Nash equilibria in potential games), and, conversely, also allows us to show that many game-theoretic algorithms can be used to solve DCOPs. By so doing, our framework can assist system designers by making the pros and cons of, and the synergies between, the various iterative approximate best-response DCOP algorithm components clear. Abstract Distributed constraint optimization problems (DCOPs) are important in many areas of computer science and optimization. In a DCOP, each variable is controlled by one of many autonomous agents, who together have the joint goal of maximizing a global objective function. A wide variety of techniques have been explored to solve such problems, and here we focus on one of the main families, namely iterative approximate best-response algorithms used as local search algorithms for DCOPs. We define these algorithms as those in which, at each iteration, agents communicate only the states of the variables under their control to their neighbours on the constraint graph, and that reason about their next state based on the messages received from their neighbours. These algorithms include the distributed stochastic algorithm and stochastic coordination algorithms, the maximum-gain messaging algorithms, the families of fictitious play and adaptive play algorithms, and algorithms that use regret-based heuristics. This family of algorithms is commonly employed in real-world systems, as they can be used in domains where communication is difficult or costly, where it is appropriate to trade timeliness off against optimality, or where hardware limitations render complete or more computationally intensive algorithms unusable. However, until now, no overarching framework has existed for analyzing this broad family of algorithms, resulting in similar and overlapping work being published independently in several different literatures. The main contribution of this paper, then, is the development of a unified analytical framework for studying such algorithms. This framework is built on our insight that when formulated as non-cooperative games, DCOPs form a subset of the class of potential games. This result allows us to prove convergence properties of iterative approximate best-response algorithms developed in the computer science literature using game-theoretic methods (which also shows that such algorithms can also be applied to the more general problem of finding Nash equilibria in potential games), and, conversely, also allows us to show that many game-theoretic algorithms can be used to solve DCOPs. By so doing, our framework can assist system designers by making the pros and cons of, and the synergies between, the various iterative approximate best-response DCOP algorithm components clear. [PUBLICATION ABSTRACT]
Author	Leslie, David S. Chapman, Archie C. Rogers, Alex Jennings, Nicholas R.
Author_xml	– sequence: 1 givenname: Archie C. surname: Chapman fullname: Chapman, Archie C. – sequence: 2 givenname: Alex surname: Rogers fullname: Rogers, Alex – sequence: 3 givenname: Nicholas R. surname: Jennings fullname: Jennings, Nicholas R. – sequence: 4 givenname: David S. surname: Leslie fullname: Leslie, David S.
BookMark	eNp9kU9P3DAQxS0EEsvCB-Bm9cIpdBznj31crShUQuoBOEeOMwbTxE5tp1349HihJyr1ZGne7z2P3pyQQ-cdEnLO4JIBa7_eQdlIIYRkDCAPxAFZsaqRhQCoD8lqLxd7_ZicxPicEc6Ar8huQxdnzYt1j9QENeEfH35S4wO1CYNK9jdSNc_B7-ykEtIeYyoCxtm7mJXx0Qebnqb4bhlsTMH2S8KB6gykoKxL1M_JTvY1h3lHc1Q_4hRPyZFRY8Szv--aPHy7ut_eFLc_rr9vN7eF5i2kQusKGmxqDrUqSynrlg0GRVNrUeIguKqkqfQgUMvcQw3Y14brCoeqN7Lihq_JxUdu_vjXkrfvJhs1jqNy6JfYyYZnZ1NDJr98Ip_9ElxerpPQQNuWIDPEPiAdfIwBTTeH3Ex46Rh0-0t0_1wie9pPHm3Texv7gsb_ON8AP4CRsw
CitedBy_id	crossref_primary_10_1111_exsy_12293 crossref_primary_10_1177_0278364915595707 crossref_primary_10_1016_j_rcim_2011_12_001 crossref_primary_10_1080_21680566_2018_1517059 crossref_primary_10_1016_j_artint_2010_11_001 crossref_primary_10_1007_s10489_017_0905_4 crossref_primary_10_1016_j_engappai_2016_08_008 crossref_primary_10_1016_j_robot_2014_10_018 crossref_primary_10_1137_120893501 crossref_primary_10_1016_j_comnet_2016_03_001 crossref_primary_10_1080_01691864_2014_902328 crossref_primary_10_7717_peerj_cs_1296
Cites_doi	10.1109/18.825794 10.1006/jeth.1996.0014 10.2307/1969530 10.2307/2951699 10.1007/978-1-4615-0363-7_11 10.1063/1.1699114 10.1111/1468-0262.00153 10.1016/j.artint.2004.09.003 10.1613/jair.1786 10.1115/1.2766722 10.1007/978-1-4419-8909-3 10.1162/089976600300015880 10.1017/CBO9780511615320 10.1137/S0363012904439301 10.1145/1379759.1379762 10.1023/A:1007678930559 10.1016/0165-1889(94)00819-4 10.1287/moor.1060.0213 10.1145/1082473.1082536 10.7551/mitpress/2450.001.0001 10.1111/1468-0262.00376 10.1006/game.1996.0044 10.1016/j.artint.2004.10.004 10.1016/j.jet.2005.12.010 10.1017/CBO9781139168724 10.1515/9780691214252 10.1006/jeth.2000.2694 10.1109/18.910572 10.1109/IAT.2007.28 10.1016/j.artint.2004.08.004 10.1109/TRA.2002.803462 10.1093/oso/9780198572237.001.0001 10.1109/TSMCB.2009.2017273 10.2307/2951778 10.1109/TAC.2008.2010885 10.1109/MIS.2009.22 10.1006/game.1993.1021 10.1109/ICIF.2006.301807 10.1007/978-3-662-04623-4_12 10.1109/ICSMC.1999.816643 10.1126/science.220.4598.671 10.1145/1525856.1525857 10.1016/j.geb.2005.08.005 10.1109/LCOMM.2004.833817 10.1016/j.comnet.2005.09.010
ContentType	Journal Article
Copyright	Copyright © Cambridge University Press 2011
Copyright_xml	– notice: Copyright © Cambridge University Press 2011
DBID	AAYXX CITATION 3V. 7SC 7TB 7WY 7WZ 7XB 87Z 88I 8AL 8FD 8FE 8FG 8FK 8FL ABUWG AFKRA ARAPS AZQEC BENPR BEZIV BGLVJ CCPQU DWQXO FR3 FRNLG F~G GNUQQ HCIFZ JQ2 K60 K6~ K7- KR7 L.- L7M L~C L~D M0C M0N M2P P5Z P62 PHGZM PHGZT PKEHL PQBIZ PQBZA PQEST PQGLB PQQKQ PQUKI Q9U
DOI	10.1017/S0269888911000178
DatabaseName	CrossRef ProQuest Central (Corporate) Computer and Information Systems Abstracts Mechanical & Transportation Engineering Abstracts ABI/INFORM Collection ABI/INFORM Global (PDF only) ProQuest Central (purchase pre-March 2016) ABI/INFORM Collection Science Database (Alumni Edition) Computing Database (Alumni Edition) Technology Research Database ProQuest SciTech Collection ProQuest Technology Collection ProQuest Central (Alumni) (purchase pre-March 2016) ABI/INFORM Collection (Alumni) ProQuest Central (Alumni) ProQuest Central UK/Ireland Advanced Technologies & Computer Science Collection ProQuest Central Essentials ProQuest Central Business Premium Collection ProQuest Technology Collection ProQuest One Community College ProQuest Central Korea Engineering Research Database Business Premium Collection (Alumni) ABI/INFORM Global (Corporate) ProQuest Central Student SciTech Premium Collection ProQuest Computer Science Collection ProQuest Business Collection (Alumni Edition) ProQuest Business Collection Computer Science Database Civil Engineering Abstracts ABI/INFORM Professional Advanced Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional ProQuest ABI/INFORM Global Computing Database Science Database Advanced Technologies & Aerospace Database ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Premium ProQuest One Academic (New) ProQuest One Academic Middle East (New) ProQuest One Business ProQuest One Business (Alumni) ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central Basic
DatabaseTitle	CrossRef ABI/INFORM Global (Corporate) ProQuest Business Collection (Alumni Edition) ProQuest One Business Computer Science Database ProQuest Central Student Technology Collection Technology Research Database Computer and Information Systems Abstracts – Academic ProQuest One Academic Middle East (New) Mechanical & Transportation Engineering Abstracts ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Essentials ProQuest Computer Science Collection Computer and Information Systems Abstracts ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College ABI/INFORM Complete ProQuest Central ABI/INFORM Professional Advanced ProQuest One Applied & Life Sciences ProQuest Central Korea ProQuest Central (New) Advanced Technologies Database with Aerospace ABI/INFORM Complete (Alumni Edition) Advanced Technologies & Aerospace Collection Business Premium Collection Civil Engineering Abstracts ABI/INFORM Global ProQuest Computing ProQuest Science Journals (Alumni Edition) ABI/INFORM Global (Alumni Edition) ProQuest Central Basic ProQuest Science Journals ProQuest Computing (Alumni Edition) ProQuest One Academic Eastern Edition ProQuest Technology Collection ProQuest SciTech Collection ProQuest Business Collection Computer and Information Systems Abstracts Professional Advanced Technologies & Aerospace Database ProQuest One Academic UKI Edition ProQuest One Business (Alumni) Engineering Research Database ProQuest One Academic ProQuest Central (Alumni) ProQuest One Academic (New) Business Premium Collection (Alumni)
DatabaseTitleList	Civil Engineering Abstracts ABI/INFORM Global (Corporate) CrossRef
Database_xml	– sequence: 1 dbid: BENPR name: ProQuest Central url: https://www.proquest.com/central sourceTypes: Aggregation Database
DeliveryMethod	fulltext_linktorsrc
Discipline	Computer Science
EISSN	1469-8005
EndPage	444
ExternalDocumentID	2520417911 10_1017_S0269888911000178
Genre	Feature
GroupedDBID	-1D -1F -2P -2V -E. -~6 -~N .DC .FH 09C 09E 0E1 0R~ 123 29L 4.4 5VS 6~7 74X 74Y 7WY 7~V 88I 8FE 8FG 8FL 8R4 8R5 9M5 AAAZR AABES AABWE AACJH AAFUK AAGFV AAKNA AAKTX AAMNQ AANRG AARAB AASVR AATMM AAUIS AAUKB AAYXX ABBXD ABBZL ABGDZ ABITZ ABJNI ABKKG ABLJU ABMWE ABQTM ABQWD ABROB ABTCQ ABUWG ABVFV ABVKB ABXAU ABXHF ABZCX ABZUI ACAJB ACBMC ACDLN ACEJA ACETC ACGFS ACGOD ACIMK ACIWK ACRPL ACUIJ ACYZP ACZBM ACZUX ACZWT ADCGK ADDNB ADFEC ADKIL ADMLS ADNMO ADOVH ADOVT ADVJH AEBAK AEBPU AEHGV AEMFK AEMTW AENCP AENEX AENGE AFFHD AFFUJ AFKQG AFKRA AFLOS AFLVW AFUTZ AFZFC AGABE AGBYD AGJUD AGLWM AGQPQ AHQXX AHRGI AIGNW AIHIV AIOIP AISIE AJ7 AJCYY AJPFC AJQAS AKMAY AKZCZ ALMA_UNASSIGNED_HOLDINGS ALVPG ALWZO ANOYL AQJOH ARABE ARAPS ARZZG ATUCA AUXHV AYIQA AZQEC BBLKV BCGOX BENPR BESQT BEZIV BGHMG BGLVJ BJBOZ BLZWO BMAJL BPHCQ BQFHP C0O CAG CBIIA CCPQU CCQAD CCTKK CCUQV CDIZJ CFAFE CFBFF CGQII CHEAL CITATION CJCSC COF CS3 DC4 DOHLZ DU5 DWQXO EBS EGQIC EJD FRNLG GNUQQ HCIFZ HG- HST HZ~ I.6 I.7 I.9 IH6 IOEEP IOO IPYYG IS6 I~P J36 J38 J3A JHPGK JQKCU K60 K6V K6~ K7- KAFGG KCGVB KFECR L98 LHUNA LW7 M-V M0C M2P M7~ M8. NIKVX NMFBF NZEOI O9- OYBOY P2P P62 PHGZM PHGZT PQBIZ PQBZA PQGLB PQQKQ PROAC PYCCK Q2X RAMDC RCA ROL RR0 S6- S6U SAAAG T9M UT1 WFFJZ WQ3 WXU WYP ZDLDU ZJOSE ZMEZD ZYDXJ ~V1 3V. 7SC 7TB 7XB 8AL 8FD 8FK FR3 JQ2 KR7 L.- L7M L~C L~D M0N PKEHL PQEST PQUKI Q9U PUEGO
ID	FETCH-LOGICAL-c370t-cc406e65305a2299571dfe865c82ed83a49f4cd8ec910150eb5f3c4ed4bf943f3
IEDL.DBID	M0C
ISICitedReferencesCount	24
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000298544100002&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	0269-8889
IngestDate	Fri Sep 05 11:14:43 EDT 2025 Fri Jul 25 23:39:34 EDT 2025 Tue Nov 18 22:43:59 EST 2025 Sat Nov 29 04:57:21 EST 2025
IsPeerReviewed	true
IsScholarly	true
Issue	4
Language	English
License	https://www.cambridge.org/core/terms
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c370t-cc406e65305a2299571dfe865c82ed83a49f4cd8ec910150eb5f3c4ed4bf943f3
Notes	SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 14 ObjectType-Article-2 content type line 23
PQID	906077209
PQPubID	36715
PageCount	34
ParticipantIDs	proquest_miscellaneous_963910650 proquest_journals_906077209 crossref_primary_10_1017_S0269888911000178 crossref_citationtrail_10_1017_S0269888911000178
PublicationCentury	2000
PublicationDate	2011-12-00 20111201
PublicationDateYYYYMMDD	2011-12-01
PublicationDate_xml	– month: 12 year: 2011 text: 2011-12-00
PublicationDecade	2010
PublicationPlace	Cambridge
PublicationPlace_xml	– name: Cambridge
PublicationTitle	Knowledge engineering review
PublicationYear	2011
Publisher	Cambridge University Press
Publisher_xml	– name: Cambridge University Press
References	Brown (S0269888911000178_ref12) 1951 S0269888911000178_ref39 S0269888911000178_ref36 S0269888911000178_ref37 S0269888911000178_ref34 S0269888911000178_ref35 Blum (S0269888911000178_ref10) 2007; 8 Roughgarden (S0269888911000178_ref54) 2005 S0269888911000178_ref32 S0269888911000178_ref33 S0269888911000178_ref30 S0269888911000178_ref31 Anderson (S0269888911000178_ref2) 1992 S0269888911000178_ref49 Mailler (S0269888911000178_ref38) 2006; 25 S0269888911000178_ref47 S0269888911000178_ref48 S0269888911000178_ref45 S0269888911000178_ref46 Young (S0269888911000178_ref65) 1998 S0269888911000178_ref44 S0269888911000178_ref41 S0269888911000178_ref42 S0269888911000178_ref40 S0269888911000178_ref18 S0269888911000178_ref1 S0269888911000178_ref19 S0269888911000178_ref16 Cooper (S0269888911000178_ref14) 2007 S0269888911000178_ref17 Grimmett (S0269888911000178_ref22) 2001 S0269888911000178_ref58 S0269888911000178_ref15 S0269888911000178_ref59 S0269888911000178_ref5 S0269888911000178_ref56 S0269888911000178_ref4 S0269888911000178_ref3 Fudenberg (S0269888911000178_ref20) 1998 S0269888911000178_ref57 S0269888911000178_ref9 S0269888911000178_ref8 S0269888911000178_ref7 S0269888911000178_ref11 S0269888911000178_ref55 S0269888911000178_ref52 S0269888911000178_ref53 S0269888911000178_ref50 Mezzetti (S0269888911000178_ref43) 2001; 98 S0269888911000178_ref29 van Leeuwen (S0269888911000178_ref61) 2002 S0269888911000178_ref27 S0269888911000178_ref28 S0269888911000178_ref25 S0269888911000178_ref26 S0269888911000178_ref23 S0269888911000178_ref67 S0269888911000178_ref68 S0269888911000178_ref24 Chapman (S0269888911000178_ref13) 2009 Press (S0269888911000178_ref51) 1992 S0269888911000178_ref21 Aumann (S0269888911000178_ref6) 1959 S0269888911000178_ref66 S0269888911000178_ref63 S0269888911000178_ref64 S0269888911000178_ref62 S0269888911000178_ref60
References_xml	– ident: S0269888911000178_ref1 doi: 10.1109/18.825794 – ident: S0269888911000178_ref36 – ident: S0269888911000178_ref45 doi: 10.1006/jeth.1996.0014 – ident: S0269888911000178_ref52 doi: 10.2307/1969530 – ident: S0269888911000178_ref15 doi: 10.2307/2951699 – ident: S0269888911000178_ref17 doi: 10.1007/978-1-4615-0363-7_11 – start-page: 374 volume-title: Activity Analysis of Production and Allocation year: 1951 ident: S0269888911000178_ref12 – ident: S0269888911000178_ref42 doi: 10.1063/1.1699114 – ident: S0269888911000178_ref23 doi: 10.1111/1468-0262.00153 – ident: S0269888911000178_ref44 doi: 10.1016/j.artint.2004.09.003 – volume: 25 start-page: 529 year: 2006 ident: S0269888911000178_ref38 article-title: Asynchronous partial overlay: a new algorithm for solving distributed constraint satisfaction problems publication-title: Journal of Artificial Intelligence Research doi: 10.1613/jair.1786 – ident: S0269888911000178_ref5 doi: 10.1115/1.2766722 – ident: S0269888911000178_ref60 doi: 10.1007/978-1-4419-8909-3 – ident: S0269888911000178_ref37 – ident: S0269888911000178_ref62 doi: 10.1162/089976600300015880 – ident: S0269888911000178_ref3 doi: 10.1017/CBO9780511615320 – ident: S0269888911000178_ref64 – ident: S0269888911000178_ref16 – ident: S0269888911000178_ref7 doi: 10.1137/S0363012904439301 – ident: S0269888911000178_ref48 doi: 10.1145/1379759.1379762 – start-page: 287 volume-title: Contributions to the Theory of Games IV year: 1959 ident: S0269888911000178_ref6 – volume-title: The Theory of Learning in Games year: 1998 ident: S0269888911000178_ref20 – ident: S0269888911000178_ref47 – ident: S0269888911000178_ref56 doi: 10.1023/A:1007678930559 – ident: S0269888911000178_ref19 doi: 10.1016/0165-1889(94)00819-4 – ident: S0269888911000178_ref57 – ident: S0269888911000178_ref8 doi: 10.1287/moor.1060.0213 – ident: S0269888911000178_ref63 doi: 10.1145/1082473.1082536 – start-page: 252 volume-title: Electronic Notes in Theoretical Computer Science year: 2002 ident: S0269888911000178_ref61 – ident: S0269888911000178_ref34 – volume-title: Discrete Choice Theory of Product Differentiation year: 1992 ident: S0269888911000178_ref2 doi: 10.7551/mitpress/2450.001.0001 – ident: S0269888911000178_ref11 – ident: S0269888911000178_ref28 doi: 10.1111/1468-0262.00376 – ident: S0269888911000178_ref46 doi: 10.1006/game.1996.0044 – ident: S0269888911000178_ref68 doi: 10.1016/j.artint.2004.10.004 – ident: S0269888911000178_ref67 – ident: S0269888911000178_ref9 doi: 10.1016/j.jet.2005.12.010 – ident: S0269888911000178_ref59 doi: 10.1017/CBO9781139168724 – volume-title: Individual Strategy and Social Structure: An Evolutionary Theory of Institutions year: 1998 ident: S0269888911000178_ref65 doi: 10.1515/9780691214252 – volume: 98 start-page: 55 year: 2001 ident: S0269888911000178_ref43 article-title: Learning in games by random sampling publication-title: Journal of Economic Theory doi: 10.1006/jeth.2000.2694 – ident: S0269888911000178_ref50 – ident: S0269888911000178_ref33 doi: 10.1109/18.910572 – ident: S0269888911000178_ref32 doi: 10.1109/IAT.2007.28 – ident: S0269888911000178_ref27 doi: 10.1016/j.artint.2004.08.004 – ident: S0269888911000178_ref21 doi: 10.1109/TRA.2002.803462 – volume-title: Selfish Routing and the Price of Anarchy year: 2005 ident: S0269888911000178_ref54 – ident: S0269888911000178_ref58 – volume-title: Probability and Random Processes year: 2001 ident: S0269888911000178_ref22 doi: 10.1093/oso/9780198572237.001.0001 – ident: S0269888911000178_ref39 doi: 10.1109/TSMCB.2009.2017273 – volume: 8 start-page: 1307 year: 2007 ident: S0269888911000178_ref10 article-title: From external to internal regret publication-title: Journal of Machine Learning Research – volume-title: Numerical Recipes: The Art of Scientific Computing year: 1992 ident: S0269888911000178_ref51 – ident: S0269888911000178_ref66 doi: 10.2307/2951778 – start-page: 915 volume-title: Proceedings of the 8th International Conference on Autonomous Agents and Multiagent Systems (AAMAS-09) year: 2009 ident: S0269888911000178_ref13 – ident: S0269888911000178_ref40 doi: 10.1109/TAC.2008.2010885 – ident: S0269888911000178_ref53 doi: 10.1109/MIS.2009.22 – ident: S0269888911000178_ref18 doi: 10.1006/game.1993.1021 – ident: S0269888911000178_ref49 – start-page: 68 volume-title: Proceedings of the 20th Internation Joint Conference on Artificial Intelligence (IJCAI-07) year: 2007 ident: S0269888911000178_ref14 – ident: S0269888911000178_ref41 doi: 10.1109/ICIF.2006.301807 – ident: S0269888911000178_ref24 doi: 10.1007/978-3-662-04623-4_12 – ident: S0269888911000178_ref31 doi: 10.1109/ICSMC.1999.816643 – ident: S0269888911000178_ref30 doi: 10.1126/science.220.4598.671 – ident: S0269888911000178_ref29 doi: 10.1145/1525856.1525857 – ident: S0269888911000178_ref35 doi: 10.1016/j.geb.2005.08.005 – ident: S0269888911000178_ref4 – ident: S0269888911000178_ref25 doi: 10.1109/LCOMM.2004.833817 – ident: S0269888911000178_ref55 – ident: S0269888911000178_ref26 doi: 10.1016/j.comnet.2005.09.010
SSID	ssj0013103
Score	2.1039588
Snippet	Distributed constraint optimization problems (DCOPs) are important in many areas of computer science and optimization. In a DCOP, each variable is controlled... Abstract Distributed constraint optimization problems (DCOPs) are important in many areas of computer science and optimization. In a DCOP, each variable is...
SourceID	proquest crossref
SourceType	Aggregation Database Enrichment Source Index Database
StartPage	411
SubjectTerms	Algorithms Approximation Communication Computer science Dynamic programming Game theory Games Iterative methods Mathematical analysis Mathematical models Optimization Optimization techniques Sensors Taxonomy Utility functions Variables
Title	A unifying framework for iterative approximate best-response algorithms for distributed constraint optimization problems
URI	https://www.proquest.com/docview/906077209 https://www.proquest.com/docview/963910650
Volume	26
WOSCitedRecordID	wos000298544100002&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: PRVPQU databaseName: ABI/INFORM Collection customDbUrl: eissn: 1469-8005 dateEnd: 20241214 omitProxy: false ssIdentifier: ssj0013103 issn: 0269-8889 databaseCode: 7WY dateStart: 20010301 isFulltext: true titleUrlDefault: https://www.proquest.com/abicomplete providerName: ProQuest – providerCode: PRVPQU databaseName: Advanced Technologies & Aerospace Database customDbUrl: eissn: 1469-8005 dateEnd: 20241214 omitProxy: false ssIdentifier: ssj0013103 issn: 0269-8889 databaseCode: P5Z dateStart: 20010301 isFulltext: true titleUrlDefault: https://search.proquest.com/hightechjournals providerName: ProQuest – providerCode: PRVPQU databaseName: Computer Science Database customDbUrl: eissn: 1469-8005 dateEnd: 20241214 omitProxy: false ssIdentifier: ssj0013103 issn: 0269-8889 databaseCode: K7- dateStart: 20010301 isFulltext: true titleUrlDefault: http://search.proquest.com/compscijour providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest ABI/INFORM Global customDbUrl: eissn: 1469-8005 dateEnd: 20241214 omitProxy: false ssIdentifier: ssj0013103 issn: 0269-8889 databaseCode: M0C dateStart: 20010301 isFulltext: true titleUrlDefault: https://search.proquest.com/abiglobal providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 1469-8005 dateEnd: 20241214 omitProxy: false ssIdentifier: ssj0013103 issn: 0269-8889 databaseCode: BENPR dateStart: 20010301 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: Science Database customDbUrl: eissn: 1469-8005 dateEnd: 20241214 omitProxy: false ssIdentifier: ssj0013103 issn: 0269-8889 databaseCode: M2P dateStart: 20010301 isFulltext: true titleUrlDefault: https://search.proquest.com/sciencejournals providerName: ProQuest
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1LS8QwEB58Hbz4FtfVJQdPQjBt09dJVBRBXBZRfFxKm6a6oF3ddmV_vjNpuiKCFy-BNo-GTjLzJZnMB3Agpcgy5UuehsrnaAI0j0WQ8iB3HB24GjF5ZMgmwn4_eniIB9Y3p7Jula1ONIo6HynaIz_CFgQiQREfv39wIo2iw1XLoDEPiwRsKHT-tTj7PkRwDDEyrjJijgu9uD3UNBGj8SW9o4hpxFD_0yz91MrG1Fys_rOTa7BiMSY7aQbFOszpcgNWW_4GZqfzJkxP2KQcmotOrGidtBiiWNbEWkZFyEzM8ekQca1mGXaEjxunWsx5fcZv1y9vlamSUwReIs_SOVMEOol7omYjVElv9q4ns-w11RbcXZzfnl1yy8TAlReKmiuFdl8HPiqH1EUD5odOXugo8FXk6jzyUhkXUuWRVog-UBI68wtPSZ3LrIilV3jbsFCOSr0DDFVMkOLCWOGfl0oUsSg8ug0bpU7mhkJ2QLSCSJQNU049fk0af7Qw-SW7DhzOqrw3MTr-KtxtRZfY6VolM7l1gM1ycZ7R4Ula6tEEiyCUcwjP7v7ZQBeWza6zcXjZg4V6PNH7sKQ-62E17sF8eP_Yg8XT8_7gBp-uQt4zY5ZSd4DpwH_6AqEd8tI
linkProvider	ProQuest
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1JS8QwFH6MC-jFccVxzUEvQjBt0yUHkcGFEXXwoCBeapumOqAdnXZcfpT_0Ze0HRHBmwevSROa5vV7X5KX9wFscc7iWLqcRr50KboARQXzIuollqU8WyEnD4zYhN_tBtfX4qIBH_VdGB1WWWOiAeqkL_Ue-S72wJAJMrH_9Ey1aJQ-XK0VNEqrOFXvr7hiy_dODnF6t237-OjyoEMrUQEqHZ8VVEp0Ycpz0c4jG7HY9a0kVYHnysBWSeBEXKRcJoGS6EiRLanYTR3JVcLjVHAndbDfMZhAFiE0Dly4N1-HFpYRYsZVjaC4sBT1IarJUI2FukxnaMOC4Lsb_O4FjGs7bv6zjzILMxWHJu3S6OegobJ5aNb6FKSCqwV4a5Nh1jMXuUhaB6ERZOmkzCWNQE9MTvW3HvJ2RWIcOB2UQcNY83CHYy3uH3PTJNEZhrU4mEqI1KRaa2sUpI-Q-1jdZSWVOk--CFd_Mv4lGM_6mVoGghDqRbjwlzjTXLJUsNTRt32DyIptn_EWsHriQ1mlYddv_BCW8XZ--MNWWrAzavJU5iD57eHV2lTCCo7ycGQnLSCjWsQRfTgUZao_xEeQqlqar6_82sEmTHUuz8_Cs5Pu6SpMmx12E9yzBuPFYKjWYVK-FL18sGH-DQK3f213n6J-Sxg
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=A+unifying+framework+for+iterative+approximate+best-response+algorithms+for+distributed+constraint+optimization+problems&rft.jtitle=Knowledge+engineering+review&rft.au=Chapman%2C+Archie+C&rft.au=Rogers%2C+Alex&rft.au=Jennings%2C+Nicholas+R&rft.au=Leslie%2C+David+S&rft.date=2011-12-01&rft.issn=0269-8889&rft.eissn=1469-8005&rft.volume=26&rft.issue=4&rft.spage=411&rft.epage=444&rft_id=info:doi/10.1017%2FS0269888911000178&rft.externalDBID=NO_FULL_TEXT
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0269-8889&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0269-8889&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0269-8889&client=summon