Learning Dynamic Adaptation Strategies in Agent-Based Traffic Simulation Experiments
Identifieur interne : 001911 ( Istex/Corpus ); précédent : 001910; suivant : 001912Learning Dynamic Adaptation Strategies in Agent-Based Traffic Simulation Experiments
Auteurs : Andreas D. Lattner ; Jörg Dallmeyer ; Ingo J. TimmSource :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2011.
Abstract
Abstract: The increase of road users and traffic load has lead to the situation that in some regions road capacities appear to be exceeded regularly. Although there is natural capacity limit of roads, there exist potentials for a dynamic adaptation of road usage. Finding out about useful rules for dynamic adaptations of traffic rules is a costly and time consuming effort if performed in the real world. In this paper, we introduce an agent-based traffic simulation model and present an approach to learning dynamic adaptation rules in traffic scenarios based on supervised learning from simulation data. For evaluation, we apply our approach to synthetic traffic scenarios. Initial results show the feasibility of the approach and indicate that learned dynamic adaptation strategies can lead to an improvement w.r.t. the average velocity in our scenarios.
Url:
DOI: 10.1007/978-3-642-24603-6_9
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Although there is natural capacity limit of roads, there exist potentials for a dynamic adaptation of road usage. Finding out about useful rules for dynamic adaptations of traffic rules is a costly and time consuming effort if performed in the real world. In this paper, we introduce an agent-based traffic simulation model and present an approach to learning dynamic adaptation rules in traffic scenarios based on supervised learning from simulation data. For evaluation, we apply our approach to synthetic traffic scenarios. Initial results show the feasibility of the approach and indicate that learned dynamic adaptation strategies can lead to an improvement w.r.t. the average velocity in our scenarios.</div></front></TEI><istex><corpusName>springer</corpusName><author><json:item><name>Andreas D. Lattner</name><affiliations><json:string>Information Systems and Simulation, Institute of Computer Science, Goethe University Frankfurt, P.O. 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Finding out about useful rules for dynamic adaptations of traffic rules is a costly and time consuming effort if performed in the real world. In this paper, we introduce an agent-based traffic simulation model and present an approach to learning dynamic adaptation rules in traffic scenarios based on supervised learning from simulation data. For evaluation, we apply our approach to synthetic traffic scenarios. Initial results show the feasibility of the approach and indicate that learned dynamic adaptation strategies can lead to an improvement w.r.t. the average velocity in our scenarios.</abstract><qualityIndicators><score>7.867</score><pdfVersion>1.6</pdfVersion><pdfPageSize>429.442 x 659.895 pts</pdfPageSize><refBibsNative>false</refBibsNative><keywordCount>0</keywordCount><abstractCharCount>858</abstractCharCount><pdfWordCount>4771</pdfWordCount><pdfCharCount>26666</pdfCharCount><pdfPageCount>12</pdfPageCount><abstractWordCount>133</abstractWordCount></qualityIndicators><title>Learning Dynamic Adaptation Strategies in Agent-Based Traffic Simulation Experiments</title><chapterId><json:string>9</json:string><json:string>Chap9</json:string></chapterId><refBibs><json:item><author><json:item><name>O Baqueiro</name></json:item><json:item><name>Y,J Wang</name></json:item><json:item><name>P Mcburney</name></json:item><json:item><name>F 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Kleinberg</name><affiliations><json:string>Cornell University, Ithaca, NY, USA</json:string></affiliations></json:item><json:item><name>Friedemann Mattern</name><affiliations><json:string>ETH Zurich, Zurich, Switzerland</json:string></affiliations></json:item><json:item><name>John C. Mitchell</name><affiliations><json:string>Stanford University, Stanford, CA, USA</json:string></affiliations></json:item><json:item><name>Moni Naor</name><affiliations><json:string>Weizmann Institute of Science, Rehovot, Israel</json:string></affiliations></json:item><json:item><name>Oscar Nierstrasz</name><affiliations><json:string>University of Bern, Bern, Switzerland</json:string></affiliations></json:item><json:item><name>C. Pandu Rangan</name><affiliations><json:string>Indian Institute of Technology, Madras, India</json:string></affiliations></json:item><json:item><name>Bernhard Steffen</name><affiliations><json:string>University of Dortmund, Dortmund, Germany</json:string></affiliations></json:item><json:item><name>Madhu Sudan</name><affiliations><json:string>Massachusetts Institute of Technology, MA, USA</json:string></affiliations></json:item><json:item><name>Demetri Terzopoulos</name><affiliations><json:string>University of California, Los Angeles, CA, USA</json:string></affiliations></json:item><json:item><name>Doug Tygar</name><affiliations><json:string>University of California, Berkeley, CA, USA</json:string></affiliations></json:item><json:item><name>Moshe Y. 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type="first">Bernhard</forename><surname>Steffen</surname></persName><affiliation>University of Dortmund, Dortmund, Germany</affiliation></editor><editor><persName><forename type="first">Madhu</forename><surname>Sudan</surname></persName><affiliation>Massachusetts Institute of Technology, MA, USA</affiliation></editor><editor><persName><forename type="first">Demetri</forename><surname>Terzopoulos</surname></persName><affiliation>University of California, Los Angeles, CA, USA</affiliation></editor><editor><persName><forename type="first">Doug</forename><surname>Tygar</surname></persName><affiliation>University of California, Berkeley, CA, USA</affiliation></editor><editor><persName><forename type="first">Moshe</forename><forename type="first">Y.</forename><surname>Vardi</surname></persName><affiliation>Rice University, Houston, TX, USA</affiliation></editor><editor><persName><forename type="first">Gerhard</forename><surname>Weikum</surname></persName><affiliation>Max-Planck Institute of Computer Science, Saarbrücken, Germany</affiliation></editor><biblScope><date>2011</date></biblScope><idno type="pISSN">0302-9743</idno><idno type="eISSN">1611-3349</idno><idno type="series-Id">558</idno></series><series><title level="s">Lecture Notes in Artificial Intelligence</title><editor><persName><forename type="first">David</forename><surname>Hutchison</surname></persName><affiliation>Lancaster University, Lancaster, UK</affiliation></editor><editor><persName><forename type="first">Takeo</forename><surname>Kanade</surname></persName><affiliation>Carnegie Mellon University, Pittsburgh, PA, USA</affiliation></editor><editor><persName><forename type="first">Josef</forename><surname>Kittler</surname></persName><affiliation>University of Surrey, Guildford, UK</affiliation></editor><editor><persName><forename type="first">Jon</forename><forename type="first">M.</forename><surname>Kleinberg</surname></persName><affiliation>Cornell University, Ithaca, NY, USA</affiliation></editor><editor><persName><forename type="first">Friedemann</forename><surname>Mattern</surname></persName><affiliation>ETH Zurich, Zurich, Switzerland</affiliation></editor><editor><persName><forename type="first">John</forename><forename type="first">C.</forename><surname>Mitchell</surname></persName><affiliation>Stanford University, Stanford, CA, USA</affiliation></editor><editor><persName><forename type="first">Moni</forename><surname>Naor</surname></persName><affiliation>Weizmann Institute of Science, Rehovot, Israel</affiliation></editor><editor><persName><forename type="first">Oscar</forename><surname>Nierstrasz</surname></persName><affiliation>University of Bern, Bern, Switzerland</affiliation></editor><editor><persName><forename type="first">C.</forename><surname>Pandu Rangan</surname></persName><affiliation>Indian Institute of Technology, Madras, India</affiliation></editor><editor><persName><forename type="first">Bernhard</forename><surname>Steffen</surname></persName><affiliation>University of Dortmund, Dortmund, Germany</affiliation></editor><editor><persName><forename type="first">Madhu</forename><surname>Sudan</surname></persName><affiliation>Massachusetts Institute of Technology, MA, USA</affiliation></editor><editor><persName><forename type="first">Demetri</forename><surname>Terzopoulos</surname></persName><affiliation>University of California, Los Angeles, CA, USA</affiliation></editor><editor><persName><forename type="first">Doug</forename><surname>Tygar</surname></persName><affiliation>University of California, Berkeley, CA, USA</affiliation></editor><editor><persName><forename type="first">Moshe</forename><forename type="first">Y.</forename><surname>Vardi</surname></persName><affiliation>Rice University, Houston, TX, USA</affiliation></editor><editor><persName><forename type="first">Gerhard</forename><surname>Weikum</surname></persName><affiliation>Max-Planck Institute of Computer Science, Saarbrücken, Germany</affiliation></editor><editor><persName><forename type="first">Randy</forename><surname>Goebel</surname></persName><affiliation>University of Alberta, Edmonton, Canada</affiliation></editor><editor><persName><forename type="first">Jörg</forename><surname>Siekmann</surname></persName><affiliation>University of Saarland, Saarbrücken, Germany</affiliation></editor><editor><persName><forename type="first">Wolfgang</forename><surname>Wahlster</surname></persName><affiliation>DFKI and University of Saarland, Saarbrücken, Germany</affiliation></editor><editor><persName><forename type="first">Franziska</forename><surname>Klügl</surname></persName><email>franziska.klugl@oru.se</email><affiliation>Modeling and Simulation Research Center, Örebro University, 70182, Örebro, Sweden</affiliation></editor><editor><persName><forename type="first">Sascha</forename><surname>Ossowski</surname></persName><email>sascha.ossowski@urjc.es</email><affiliation>University Rey Juan Carlos, ETSII / CETINIA, Calle Tulipán s/n, 28933, Móstoles (Madrid), Spain</affiliation></editor><idno type="pISSN">0302-9743</idno><idno type="eISSN">1611-3349</idno><biblScope unit="seriesId">1244</biblScope></series><idno type="istex">E24A56BEF29C753D00A01508064C0CD7A81656ED</idno><idno type="DOI">10.1007/978-3-642-24603-6_9</idno><idno type="ChapterID">9</idno><idno type="ChapterID">Chap9</idno></biblStruct></sourceDesc></fileDesc><profileDesc><creation><date>2011</date></creation><langUsage><language ident="en">en</language></langUsage><abstract xml:lang="en"><p>Abstract: The increase of road users and traffic load has lead to the situation that in some regions road capacities appear to be exceeded regularly. Although there is natural capacity limit of roads, there exist potentials for a dynamic adaptation of road usage. Finding out about useful rules for dynamic adaptations of traffic rules is a costly and time consuming effort if performed in the real world. In this paper, we introduce an agent-based traffic simulation model and present an approach to learning dynamic adaptation rules in traffic scenarios based on supervised learning from simulation data. For evaluation, we apply our approach to synthetic traffic scenarios. Initial results show the feasibility of the approach and indicate that learned dynamic adaptation strategies can lead to an improvement w.r.t. the average velocity in our scenarios.</p></abstract><textClass><keywords scheme="Book-Subject-Collection"><list><label>SUCO11645</label><item><term>Computer Science</term></item></list></keywords></textClass><textClass><keywords scheme="Book-Subject-Group"><list><label>I</label><label>I21017</label><label>I14029</label><label>I13022</label><label>I18040</label><label>I14010</label><label>I1603X</label><item><term>Computer Science</term></item><item><term>Artificial Intelligence (incl. Robotics)</term></item><item><term>Software Engineering</term></item><item><term>Computer Communication Networks</term></item><item><term>Information Systems Applications (incl. 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California</OrgName><OrgAddress><City>Berkeley</City><State>CA</State><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff14"><OrgName>Rice University</OrgName><OrgAddress><City>Houston</City><State>TX</State><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff15"><OrgName>Max-Planck Institute of Computer Science</OrgName><OrgAddress><City>Saarbrücken</City><Country>Germany</Country></OrgAddress></Affiliation></EditorGroup></SeriesHeader><SubSeries><SubSeriesInfo><SubSeriesID>1244</SubSeriesID><SubSeriesPrintISSN>0302-9743</SubSeriesPrintISSN><SubSeriesElectronicISSN>1611-3349</SubSeriesElectronicISSN><SubSeriesTitle Language="En">Lecture Notes in Artificial Intelligence</SubSeriesTitle></SubSeriesInfo><SubSeriesHeader><EditorGroup><Editor AffiliationIDS="Aff16"><EditorName DisplayOrder="Western"><GivenName>Randy</GivenName><FamilyName>Goebel</FamilyName></EditorName></Editor><Editor AffiliationIDS="Aff17"><EditorName 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TocLevels="0"><BookID>978-3-642-24603-6</BookID><BookTitle>Multiagent System Technologies</BookTitle><BookSubTitle>9th German Conference, MATES 2011, Berlin, Germany, October 6-7, 2011. Proceedings</BookSubTitle><BookVolumeNumber>6973</BookVolumeNumber><BookSequenceNumber>6973</BookSequenceNumber><BookDOI>10.1007/978-3-642-24603-6</BookDOI><BookTitleID>273525</BookTitleID><BookPrintISBN>978-3-642-24602-9</BookPrintISBN><BookElectronicISBN>978-3-642-24603-6</BookElectronicISBN><BookChapterCount>20</BookChapterCount><BookCopyright><CopyrightHolderName>Springer-Verlag GmbH Berlin Heidelberg</CopyrightHolderName><CopyrightYear>2011</CopyrightYear></BookCopyright><BookSubjectGroup><BookSubject Code="I" Type="Primary">Computer Science</BookSubject><BookSubject Code="I21017" Priority="1" Type="Secondary">Artificial Intelligence (incl. Robotics)</BookSubject><BookSubject Code="I14029" Priority="2" Type="Secondary">Software Engineering</BookSubject><BookSubject Code="I13022" Priority="3" Type="Secondary">Computer Communication Networks</BookSubject><BookSubject Code="I18040" Priority="4" Type="Secondary">Information Systems Applications (incl. Internet)</BookSubject><BookSubject Code="I14010" Priority="5" Type="Secondary">Programming Techniques</BookSubject><BookSubject Code="I1603X" Priority="6" Type="Secondary">Logics and Meanings of Programs</BookSubject><SubjectCollection Code="SUCO11645">Computer Science</SubjectCollection></BookSubjectGroup><BookContext><SeriesID>558</SeriesID><SubSeriesID>1244</SubSeriesID></BookContext></BookInfo><BookHeader><EditorGroup><Editor AffiliationIDS="Aff19"><EditorName DisplayOrder="Western"><GivenName>Franziska</GivenName><FamilyName>Klügl</FamilyName></EditorName><Contact><Email>franziska.klugl@oru.se</Email></Contact></Editor><Editor AffiliationIDS="Aff20"><EditorName DisplayOrder="Western"><GivenName>Sascha</GivenName><FamilyName>Ossowski</FamilyName></EditorName><Contact><Email>sascha.ossowski@urjc.es</Email></Contact></Editor><Affiliation ID="Aff19"><OrgDivision>Modeling and Simulation Research Center</OrgDivision><OrgName>Örebro University</OrgName><OrgAddress><Postcode>70182</Postcode><City>Örebro</City><Country>Sweden</Country></OrgAddress></Affiliation><Affiliation ID="Aff20"><OrgName>University Rey Juan Carlos, ETSII / CETINIA</OrgName><OrgAddress><Street>Calle Tulipán s/n</Street><Postcode>28933</Postcode><City>Móstoles (Madrid)</City><Country>Spain</Country></OrgAddress></Affiliation></EditorGroup></BookHeader><Chapter ID="Chap9" Language="En"><ChapterInfo ChapterType="OriginalPaper" ContainsESM="No" NumberingDepth="2" NumberingStyle="ContentOnly" TocLevels="0"><ChapterID>9</ChapterID><ChapterDOI>10.1007/978-3-642-24603-6_9</ChapterDOI><ChapterSequenceNumber>9</ChapterSequenceNumber><ChapterTitle Language="En">Learning Dynamic Adaptation Strategies in Agent-Based Traffic Simulation Experiments</ChapterTitle><ChapterFirstPage>77</ChapterFirstPage><ChapterLastPage>88</ChapterLastPage><ChapterCopyright><CopyrightHolderName>Springer-Verlag Berlin Heidelberg</CopyrightHolderName><CopyrightYear>2011</CopyrightYear></ChapterCopyright><ChapterGrants Type="Regular"><MetadataGrant Grant="OpenAccess"></MetadataGrant><AbstractGrant Grant="OpenAccess"></AbstractGrant><BodyPDFGrant Grant="Restricted"></BodyPDFGrant><BodyHTMLGrant Grant="Restricted"></BodyHTMLGrant><BibliographyGrant Grant="Restricted"></BibliographyGrant><ESMGrant Grant="Restricted"></ESMGrant></ChapterGrants><ChapterContext><SeriesID>558</SeriesID><BookID>978-3-642-24603-6</BookID><BookTitle>Multiagent System Technologies</BookTitle></ChapterContext></ChapterInfo><ChapterHeader><AuthorGroup><Author AffiliationIDS="Aff21"><AuthorName DisplayOrder="Western"><GivenName>Andreas</GivenName><GivenName>D.</GivenName><FamilyName>Lattner</FamilyName></AuthorName></Author><Author AffiliationIDS="Aff21"><AuthorName DisplayOrder="Western"><GivenName>Jörg</GivenName><FamilyName>Dallmeyer</FamilyName></AuthorName></Author><Author AffiliationIDS="Aff22"><AuthorName DisplayOrder="Western"><GivenName>Ingo</GivenName><GivenName>J.</GivenName><FamilyName>Timm</FamilyName></AuthorName></Author><Affiliation ID="Aff21"><OrgDivision>Information Systems and Simulation, Institute of Computer Science</OrgDivision><OrgName>Goethe University Frankfurt</OrgName><OrgAddress><Postbox>P.O. Box 11 19 32</Postbox><Postcode>60054</Postcode><City>Frankfurt</City><Country>Germany</Country></OrgAddress></Affiliation><Affiliation ID="Aff22"><OrgDivision>Business Informatics I</OrgDivision><OrgName>University of Trier</OrgName><OrgAddress><Postcode>D-54286</Postcode><City>Trier</City><Country>Germany</Country></OrgAddress></Affiliation></AuthorGroup><Abstract ID="Abs1" Language="En"><Heading>Abstract</Heading><Para>The increase of road users and traffic load has lead to the situation that in some regions road capacities appear to be exceeded regularly. Although there is natural capacity limit of roads, there exist potentials for a dynamic adaptation of road usage. Finding out about useful rules for dynamic adaptations of traffic rules is a costly and time consuming effort if performed in the real world. In this paper, we introduce an agent-based traffic simulation model and present an approach to learning dynamic adaptation rules in traffic scenarios based on supervised learning from simulation data. For evaluation, we apply our approach to synthetic traffic scenarios. Initial results show the feasibility of the approach and indicate that learned dynamic adaptation strategies can lead to an improvement w.r.t. the average velocity in our scenarios.</Para></Abstract><KeywordGroup Language="En"><Heading>Keywords</Heading><Keyword>Agent-based traffic simulation</Keyword><Keyword>supervised learning</Keyword><Keyword>adaptation strategies</Keyword></KeywordGroup></ChapterHeader><NoBody></NoBody></Chapter></Book></Series></Publisher></istex:document></istex:metadataXml><mods version="3.6"><titleInfo lang="en"><title>Learning Dynamic Adaptation Strategies in Agent-Based Traffic Simulation Experiments</title></titleInfo><titleInfo type="alternative" contentType="CDATA" lang="en"><title>Learning Dynamic Adaptation Strategies in Agent-Based Traffic Simulation Experiments</title></titleInfo><name type="personal"><namePart type="given">Andreas</namePart><namePart type="given">D.</namePart><namePart type="family">Lattner</namePart><affiliation>Information Systems and Simulation, Institute of Computer Science, Goethe University Frankfurt, P.O. Box 11 19 32, 60054, Frankfurt, Germany</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Jörg</namePart><namePart type="family">Dallmeyer</namePart><affiliation>Information Systems and Simulation, Institute of Computer Science, Goethe University Frankfurt, P.O. Box 11 19 32, 60054, Frankfurt, Germany</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Ingo</namePart><namePart type="given">J.</namePart><namePart type="family">Timm</namePart><affiliation>Business Informatics I, University of Trier, D-54286, Trier, Germany</affiliation><role><roleTerm type="text">author</roleTerm></role></name><typeOfResource>text</typeOfResource><genre type="conference" displayLabel="OriginalPaper"></genre><originInfo><publisher>Springer Berlin Heidelberg</publisher><place><placeTerm type="text">Berlin, Heidelberg</placeTerm></place><dateIssued encoding="w3cdtf">2011</dateIssued><copyrightDate encoding="w3cdtf">2011</copyrightDate></originInfo><language><languageTerm type="code" authority="rfc3066">en</languageTerm><languageTerm type="code" authority="iso639-2b">eng</languageTerm></language><physicalDescription><internetMediaType>text/html</internetMediaType></physicalDescription><abstract lang="en">Abstract: The increase of road users and traffic load has lead to the situation that in some regions road capacities appear to be exceeded regularly. Although there is natural capacity limit of roads, there exist potentials for a dynamic adaptation of road usage. Finding out about useful rules for dynamic adaptations of traffic rules is a costly and time consuming effort if performed in the real world. In this paper, we introduce an agent-based traffic simulation model and present an approach to learning dynamic adaptation rules in traffic scenarios based on supervised learning from simulation data. For evaluation, we apply our approach to synthetic traffic scenarios. Initial results show the feasibility of the approach and indicate that learned dynamic adaptation strategies can lead to an improvement w.r.t. the average velocity in our scenarios.</abstract><relatedItem type="host"><titleInfo><title>Multiagent System Technologies</title><subTitle>9th German Conference, MATES 2011, Berlin, Germany, October 6-7, 2011. 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type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">C.</namePart><namePart type="family">Pandu Rangan</namePart><affiliation>Indian Institute of Technology, Madras, India</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Bernhard</namePart><namePart type="family">Steffen</namePart><affiliation>University of Dortmund, Dortmund, Germany</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Madhu</namePart><namePart type="family">Sudan</namePart><affiliation>Massachusetts Institute of Technology, MA, USA</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Demetri</namePart><namePart type="family">Terzopoulos</namePart><affiliation>University of California, Los Angeles, CA, USA</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Doug</namePart><namePart type="family">Tygar</namePart><affiliation>University of California, Berkeley, CA, USA</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Moshe</namePart><namePart type="given">Y.</namePart><namePart type="family">Vardi</namePart><affiliation>Rice University, Houston, TX, USA</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Gerhard</namePart><namePart type="family">Weikum</namePart><affiliation>Max-Planck Institute of Computer Science, Saarbrücken, Germany</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><originInfo><copyrightDate encoding="w3cdtf">2011</copyrightDate><issuance>serial</issuance></originInfo><relatedItem type="constituent"><titleInfo><title>Lecture Notes in Artificial Intelligence</title></titleInfo><name type="personal"><namePart type="given">David</namePart><namePart type="family">Hutchison</namePart><affiliation>Lancaster University, Lancaster, UK</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Takeo</namePart><namePart type="family">Kanade</namePart><affiliation>Carnegie Mellon University, Pittsburgh, PA, USA</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Josef</namePart><namePart type="family">Kittler</namePart><affiliation>University of Surrey, Guildford, UK</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Jon</namePart><namePart type="given">M.</namePart><namePart type="family">Kleinberg</namePart><affiliation>Cornell University, Ithaca, NY, USA</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Friedemann</namePart><namePart type="family">Mattern</namePart><affiliation>ETH Zurich, Zurich, Switzerland</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">John</namePart><namePart type="given">C.</namePart><namePart type="family">Mitchell</namePart><affiliation>Stanford University, Stanford, CA, USA</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Moni</namePart><namePart type="family">Naor</namePart><affiliation>Weizmann Institute of Science, Rehovot, Israel</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Oscar</namePart><namePart type="family">Nierstrasz</namePart><affiliation>University of Bern, Bern, Switzerland</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">C.</namePart><namePart type="family">Pandu Rangan</namePart><affiliation>Indian Institute of Technology, Madras, India</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Bernhard</namePart><namePart type="family">Steffen</namePart><affiliation>University of Dortmund, Dortmund, Germany</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Madhu</namePart><namePart type="family">Sudan</namePart><affiliation>Massachusetts Institute of Technology, MA, USA</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Demetri</namePart><namePart type="family">Terzopoulos</namePart><affiliation>University of California, Los Angeles, CA, USA</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Doug</namePart><namePart type="family">Tygar</namePart><affiliation>University of California, Berkeley, CA, USA</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Moshe</namePart><namePart type="given">Y.</namePart><namePart type="family">Vardi</namePart><affiliation>Rice University, Houston, TX, USA</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Gerhard</namePart><namePart type="family">Weikum</namePart><affiliation>Max-Planck Institute of Computer Science, Saarbrücken, Germany</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Randy</namePart><namePart type="family">Goebel</namePart><affiliation>University of Alberta, Edmonton, Canada</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Jörg</namePart><namePart type="family">Siekmann</namePart><affiliation>University of Saarland, Saarbrücken, Germany</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Wolfgang</namePart><namePart type="family">Wahlster</namePart><affiliation>DFKI and University of Saarland, Saarbrücken, Germany</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Franziska</namePart><namePart type="family">Klügl</namePart><affiliation>Modeling and Simulation Research Center, Örebro University, 70182, Örebro, Sweden</affiliation><affiliation>E-mail: franziska.klugl@oru.se</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Sascha</namePart><namePart type="family">Ossowski</namePart><affiliation>University Rey Juan Carlos, ETSII / CETINIA, Calle Tulipán s/n, 28933, Móstoles (Madrid), Spain</affiliation><affiliation>E-mail: sascha.ossowski@urjc.es</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><genre type="sub-series"></genre><identifier type="ISSN">0302-9743</identifier><identifier type="eISSN">1611-3349</identifier><identifier type="SubSeriesID">1244</identifier></relatedItem><identifier type="ISSN">0302-9743</identifier><identifier type="eISSN">1611-3349</identifier><identifier type="SeriesID">558</identifier><recordInfo><recordOrigin>Springer-Verlag GmbH Berlin Heidelberg, 2011</recordOrigin></recordInfo></relatedItem><identifier type="istex">E24A56BEF29C753D00A01508064C0CD7A81656ED</identifier><identifier type="DOI">10.1007/978-3-642-24603-6_9</identifier><identifier type="ChapterID">9</identifier><identifier type="ChapterID">Chap9</identifier><accessCondition type="use and reproduction" contentType="copyright">Springer-Verlag GmbH Berlin Heidelberg, 2011</accessCondition><recordInfo><recordContentSource>SPRINGER</recordContentSource><recordOrigin>Springer-Verlag Berlin Heidelberg, 2011</recordOrigin></recordInfo></mods></metadata></istex></record>Pour manipuler ce document 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