Guiding User Adaptation in Serious Games
Identifieur interne : 001153 ( Istex/Corpus ); précédent : 001152; suivant : 001154Guiding User Adaptation in Serious Games
Auteurs : Joost Westra ; Frank Dignum ; Virginia DignumSource :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2011.
Abstract
Abstract: The complexity of training situations requires teaching different skills to different trainees and in different situations. Current approaches of dynamic difficulty adjustment in games use a purely centralized approach for this adaptation. This becomes impractical if the complexity increases and especially if past actions of the non player characters need to be taken into account. Agents are increasingly used in serious game implementations as a means to reduce complexity and increase believability. Agents can be designed to adapt their behavior to different user requirements and situations. However, this leads to situations in which the lack of coordination between the agents makes it practically impossible to follow the intended storyline of the game and select suitable difficulties for the trainee. In this paper, we present a monitoring system for the coordination of the characters actions and adaptation to guarantee appropriate combinations of character actions that ensure the preservation of the storyline. In particular we propose an architecture for game design that introduces a monitoring module to check the development of user skills and direct coordinated agent adaptation. That is, agents propose possible courses of action that are fitting their role and context, and the monitor module uses this information together with its evaluation of user level and storyline progress to determine the most suitable combination of proposals. Categories and Subject Descriptors: I.2.11 [Artificial Intelligence]: Distributed Artificial Intelligence
Url:
DOI: 10.1007/978-3-642-18181-8_9
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ISTEX:08068A218D5A8E102205F4628911DBFBA1E07873Le document en format XML
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Current approaches of dynamic difficulty adjustment in games use a purely centralized approach for this adaptation. This becomes impractical if the complexity increases and especially if past actions of the non player characters need to be taken into account. Agents are increasingly used in serious game implementations as a means to reduce complexity and increase believability. Agents can be designed to adapt their behavior to different user requirements and situations. However, this leads to situations in which the lack of coordination between the agents makes it practically impossible to follow the intended storyline of the game and select suitable difficulties for the trainee. In this paper, we present a monitoring system for the coordination of the characters actions and adaptation to guarantee appropriate combinations of character actions that ensure the preservation of the storyline. In particular we propose an architecture for game design that introduces a monitoring module to check the development of user skills and direct coordinated agent adaptation. That is, agents propose possible courses of action that are fitting their role and context, and the monitor module uses this information together with its evaluation of user level and storyline progress to determine the most suitable combination of proposals. Categories and Subject Descriptors: I.2.11 [Artificial Intelligence]: Distributed Artificial Intelligence</div></front></TEI><istex><corpusName>springer</corpusName><author><json:item><name>Joost Westra</name><affiliations><json:string>Universiteit Utrecht,</json:string></affiliations></json:item><json:item><name>Frank Dignum</name><affiliations><json:string>Universiteit Utrecht,</json:string></affiliations></json:item><json:item><name>Virginia Dignum</name><affiliations><json:string>Delft University of Technology,</json:string></affiliations></json:item></author><language><json:string>eng</json:string></language><abstract>Abstract: The complexity of training situations requires teaching different skills to different trainees and in different situations. Current approaches of dynamic difficulty adjustment in games use a purely centralized approach for this adaptation. This becomes impractical if the complexity increases and especially if past actions of the non player characters need to be taken into account. Agents are increasingly used in serious game implementations as a means to reduce complexity and increase believability. Agents can be designed to adapt their behavior to different user requirements and situations. However, this leads to situations in which the lack of coordination between the agents makes it practically impossible to follow the intended storyline of the game and select suitable difficulties for the trainee. In this paper, we present a monitoring system for the coordination of the characters actions and adaptation to guarantee appropriate combinations of character actions that ensure the preservation of the storyline. In particular we propose an architecture for game design that introduces a monitoring module to check the development of user skills and direct coordinated agent adaptation. That is, agents propose possible courses of action that are fitting their role and context, and the monitor module uses this information together with its evaluation of user level and storyline progress to determine the most suitable combination of proposals. 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Box 80.089, 3508, Utrecht, TB, The Netherlands</affiliation></editor><idno type="pISSN">0302-9743</idno><idno type="eISSN">1611-3349</idno><biblScope type="seriesId">1244</biblScope></series><idno type="istex">08068A218D5A8E102205F4628911DBFBA1E07873</idno><idno type="DOI">10.1007/978-3-642-18181-8_9</idno><idno type="ChapterID">Chap9</idno><idno type="ChapterID">9</idno></biblStruct></sourceDesc></fileDesc><profileDesc><creation><date>2011</date></creation><langUsage><language ident="en">en</language></langUsage><abstract xml:lang="en"><p>Abstract: The complexity of training situations requires teaching different skills to different trainees and in different situations. Current approaches of dynamic difficulty adjustment in games use a purely centralized approach for this adaptation. This becomes impractical if the complexity increases and especially if past actions of the non player characters need to be taken into account. Agents are increasingly used in serious game implementations as a means to reduce complexity and increase believability. Agents can be designed to adapt their behavior to different user requirements and situations. However, this leads to situations in which the lack of coordination between the agents makes it practically impossible to follow the intended storyline of the game and select suitable difficulties for the trainee. In this paper, we present a monitoring system for the coordination of the characters actions and adaptation to guarantee appropriate combinations of character actions that ensure the preservation of the storyline. In particular we propose an architecture for game design that introduces a monitoring module to check the development of user skills and direct coordinated agent adaptation. That is, agents propose possible courses of action that are fitting their role and context, and the monitor module uses this information together with its evaluation of user level and storyline progress to determine the most suitable combination of proposals. Categories and Subject Descriptors: I.2.11 [Artificial Intelligence]: Distributed Artificial Intelligence</p></abstract><textClass><classCode scheme="General Terms">Design</classCode></textClass><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>I13022</label><label>I14029</label><label>I16013</label><label>I21025</label><label>I13030</label><item><term>Computer Science</term></item><item><term>Artificial Intelligence (incl. 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University</OrgName><OrgAddress><City>Stanford</City><State>CA</State><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff7"><OrgName>Weizmann Institute of Science</OrgName><OrgAddress><City>Rehovot</City><Country>Israel</Country></OrgAddress></Affiliation><Affiliation ID="Aff8"><OrgName>University of Bern</OrgName><OrgAddress><City>Bern</City><Country>Switzerland</Country></OrgAddress></Affiliation><Affiliation ID="Aff9"><OrgName>Indian Institute of Technology</OrgName><OrgAddress><City>Madras</City><Country>India</Country></OrgAddress></Affiliation><Affiliation ID="Aff10"><OrgName>University of Dortmund</OrgName><OrgAddress><City>Dortmund</City><Country>Germany</Country></OrgAddress></Affiliation><Affiliation ID="Aff11"><OrgName>Massachusetts Institute of Technology</OrgName><OrgAddress><State>MA</State><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff12"><OrgName>University of California</OrgName><OrgAddress><City>Los Angeles</City><State>CA</State><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff13"><OrgName>University of 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 DisplayOrder="Western"><GivenName>Jörg</GivenName><FamilyName>Siekmann</FamilyName></EditorName></Editor><Editor AffiliationIDS="Aff18"><EditorName DisplayOrder="Western"><GivenName>Wolfgang</GivenName><FamilyName>Wahlster</FamilyName></EditorName></Editor><Affiliation ID="Aff16"><OrgName>University of Alberta</OrgName><OrgAddress><City>Edmonton</City><Country>Canada</Country></OrgAddress></Affiliation><Affiliation ID="Aff17"><OrgName>University of Saarland</OrgName><OrgAddress><City>Saarbrücken</City><Country>Germany</Country></OrgAddress></Affiliation><Affiliation ID="Aff18"><OrgName>DFKI and University of Saarland</OrgName><OrgAddress><City>Saarbrücken</City><Country>Germany</Country></OrgAddress></Affiliation></EditorGroup></SubSeriesHeader></SubSeries><Book Language="En"><BookInfo BookProductType="Graduate/advanced undergraduate textbook" ContainsESM="No" Language="En" MediaType="eBook" NumberingDepth="2" NumberingStyle="ContentOnly" OutputMedium="All" TocLevels="0"><BookID>978-3-642-18181-8</BookID><BookTitle>Agents for Games and Simulations II</BookTitle><BookSubTitle>Trends in Techniques, Concepts and Design</BookSubTitle><BookVolumeNumber>6525</BookVolumeNumber><BookSequenceNumber>6525</BookSequenceNumber><BookDOI>10.1007/978-3-642-18181-8</BookDOI><BookTitleID>216296</BookTitleID><BookPrintISBN>978-3-642-18180-1</BookPrintISBN><BookElectronicISBN>978-3-642-18181-8</BookElectronicISBN><BookChapterCount>14</BookChapterCount><BookCopyright><CopyrightHolderName>Springer 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="I13022" Priority="2" Type="Secondary">Computer Communication Networks</BookSubject><BookSubject Code="I14029" Priority="3" Type="Secondary">Software Engineering</BookSubject><BookSubject Code="I16013" Priority="4" Type="Secondary">Computation by Abstract Devices</BookSubject><BookSubject Code="I21025" Priority="5" Type="Secondary">Simulation and Modeling</BookSubject><BookSubject Code="I13030" Priority="6" Type="Secondary">Special Purpose and Application-Based Systems</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>Frank</GivenName><FamilyName>Dignum</FamilyName></EditorName><Contact><Email>dignum@cs.uu.nl</Email></Contact></Editor><Affiliation ID="Aff19"><OrgDivision>Institute of Information and Computing Sciences</OrgDivision><OrgName>Utrecht University</OrgName><OrgAddress><Postbox>P.O. Box 80.089</Postbox><Postcode>3508</Postcode><City>Utrecht</City><State>TB</State><Country>The Netherlands</Country></OrgAddress></Affiliation></EditorGroup></BookHeader><Part ID="Part2"><PartInfo TocLevels="0"><PartID>2</PartID><PartSequenceNumber>2</PartSequenceNumber><PartTitle>Section 2. Training and Story Lines</PartTitle><PartChapterCount>5</PartChapterCount><PartContext><SeriesID>558</SeriesID><BookTitle>Agents for Games and Simulations II</BookTitle></PartContext></PartInfo><Chapter ID="Chap9" Language="En"><ChapterInfo ChapterType="OriginalPaper" ContainsESM="No" NumberingDepth="2" NumberingStyle="ContentOnly" TocLevels="0"><ChapterID>9</ChapterID><ChapterDOI>10.1007/978-3-642-18181-8_9</ChapterDOI><ChapterSequenceNumber>9</ChapterSequenceNumber><ChapterTitle Language="En">Guiding User Adaptation in Serious Games</ChapterTitle><ChapterFirstPage>117</ChapterFirstPage><ChapterLastPage>131</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><PartID>2</PartID><BookID>978-3-642-18181-8</BookID><BookTitle>Agents for Games and Simulations II</BookTitle></ChapterContext></ChapterInfo><ChapterHeader><AuthorGroup><Author AffiliationIDS="Aff20"><AuthorName DisplayOrder="Western"><GivenName>Joost</GivenName><FamilyName>Westra</FamilyName></AuthorName></Author><Author AffiliationIDS="Aff20"><AuthorName DisplayOrder="Western"><GivenName>Frank</GivenName><FamilyName>Dignum</FamilyName></AuthorName></Author><Author AffiliationIDS="Aff21"><AuthorName DisplayOrder="Western"><GivenName>Virginia</GivenName><FamilyName>Dignum</FamilyName></AuthorName></Author><Affiliation ID="Aff20"><OrgName>Universiteit Utrecht</OrgName><OrgAddress><Country> </Country></OrgAddress></Affiliation><Affiliation ID="Aff21"><OrgName>Delft University of Technology</OrgName><OrgAddress><Country> </Country></OrgAddress></Affiliation></AuthorGroup><Abstract ID="Abs1" Language="En"><Heading>Abstract</Heading><Para>The complexity of training situations requires teaching different skills to different trainees and in different situations. Current approaches of dynamic difficulty adjustment in games use a purely centralized approach for this adaptation. This becomes impractical if the complexity increases and especially if past actions of the non player characters need to be taken into account. Agents are increasingly used in serious game implementations as a means to reduce complexity and increase believability. Agents can be designed to adapt their behavior to different user requirements and situations. However, this leads to situations in which the lack of coordination between the agents makes it practically impossible to follow the intended storyline of the game and select suitable difficulties for the trainee.</Para><Para>In this paper, we present a monitoring system for the coordination of the characters actions and adaptation to guarantee appropriate combinations of character actions that ensure the preservation of the storyline. In particular we propose an architecture for game design that introduces a monitoring module to check the development of user skills and direct coordinated agent adaptation. That is, agents propose possible courses of action that are fitting their role and context, and the monitor module uses this information together with its evaluation of user level and storyline progress to determine the most suitable combination of proposals.</Para><Para><Emphasis Type="Bold">Categories and Subject Descriptors</Emphasis>:</Para><Para>I.2.11 [<Emphasis Type="Bold">Artificial Intelligence</Emphasis>]: Distributed Artificial Intelligence</Para></Abstract><KeywordGroup Language="--"><Heading>General Terms</Heading><Keyword>Design</Keyword></KeywordGroup><KeywordGroup Language="En"><Heading>Keywords</Heading><Keyword>Agents in games and virtual environments</Keyword><Keyword>Adaptation</Keyword><Keyword>Organisations</Keyword></KeywordGroup></ChapterHeader><NoBody></NoBody></Chapter></Part></Book></Series></Publisher></istex:document></istex:metadataXml><mods version="3.6"><titleInfo lang="en"><title>Guiding User Adaptation in Serious Games</title></titleInfo><titleInfo type="alternative" contentType="CDATA" lang="en"><title>Guiding User Adaptation in Serious Games</title></titleInfo><name type="personal"><namePart type="given">Joost</namePart><namePart type="family">Westra</namePart><affiliation>Universiteit Utrecht</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Frank</namePart><namePart type="family">Dignum</namePart><affiliation>Universiteit Utrecht</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Virginia</namePart><namePart type="family">Dignum</namePart><affiliation>Delft University of Technology</affiliation><role><roleTerm type="text">author</roleTerm></role></name><typeOfResource>text</typeOfResource><genre type="collection [research-article]" 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 complexity of training situations requires teaching different skills to different trainees and in different situations. Current approaches of dynamic difficulty adjustment in games use a purely centralized approach for this adaptation. This becomes impractical if the complexity increases and especially if past actions of the non player characters need to be taken into account. Agents are increasingly used in serious game implementations as a means to reduce complexity and increase believability. Agents can be designed to adapt their behavior to different user requirements and situations. However, this leads to situations in which the lack of coordination between the agents makes it practically impossible to follow the intended storyline of the game and select suitable difficulties for the trainee. In this paper, we present a monitoring system for the coordination of the characters actions and adaptation to guarantee appropriate combinations of character actions that ensure the preservation of the storyline. In particular we propose an architecture for game design that introduces a monitoring module to check the development of user skills and direct coordinated agent adaptation. That is, agents propose possible courses of action that are fitting their role and context, and the monitor module uses this information together with its evaluation of user level and storyline progress to determine the most suitable combination of proposals. Categories and Subject Descriptors: I.2.11 [Artificial Intelligence]: Distributed Artificial Intelligence</abstract><classification displayLabel="General Terms">Design</classification><relatedItem type="host"><titleInfo><title>Agents for Games and Simulations II</title><subTitle>Trends in Techniques, Concepts and Design</subTitle></titleInfo><name type="personal"><namePart type="given">Frank</namePart><namePart type="family">Dignum</namePart><affiliation>Institute of Information and Computing Sciences, Utrecht University, P.O. Box 80.089, 3508, Utrecht, TB, The Netherlands</affiliation><affiliation>E-mail: dignum@cs.uu.nl</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><genre type="Book Series" displayLabel="Graduate/advanced undergraduate textbook"></genre><originInfo><copyrightDate encoding="w3cdtf">2011</copyrightDate><issuance>monographic</issuance></originInfo><subject><genre>Book Subject Collection</genre><topic authority="SpringerSubjectCodes" authorityURI="SUCO11645">Computer Science</topic></subject><subject><genre>Book Subject Group</genre><topic authority="SpringerSubjectCodes" authorityURI="I">Computer Science</topic><topic authority="SpringerSubjectCodes" authorityURI="I21017">Artificial Intelligence (incl. Robotics)</topic><topic authority="SpringerSubjectCodes" authorityURI="I13022">Computer Communication Networks</topic><topic authority="SpringerSubjectCodes" authorityURI="I14029">Software Engineering</topic><topic authority="SpringerSubjectCodes" authorityURI="I16013">Computation by Abstract Devices</topic><topic authority="SpringerSubjectCodes" authorityURI="I21025">Simulation and Modeling</topic><topic authority="SpringerSubjectCodes" authorityURI="I13030">Special Purpose and Application-Based Systems</topic></subject><identifier type="DOI">10.1007/978-3-642-18181-8</identifier><identifier type="ISBN">978-3-642-18180-1</identifier><identifier type="eISBN">978-3-642-18181-8</identifier><identifier type="ISSN">0302-9743</identifier><identifier type="eISSN">1611-3349</identifier><identifier type="BookTitleID">216296</identifier><identifier type="BookID">978-3-642-18181-8</identifier><identifier type="BookChapterCount">14</identifier><identifier type="BookVolumeNumber">6525</identifier><identifier type="BookSequenceNumber">6525</identifier><identifier type="PartChapterCount">5</identifier><part><date>2011</date><detail type="part"><title>Section 2. 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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, 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