Using multi-agent approach for the design of an intelligent learning environment
Identifieur interne : 002F10 ( Istex/Corpus ); précédent : 002F09; suivant : 002F11Using multi-agent approach for the design of an intelligent learning environment
Auteurs : Dong Mei Zhang ; Leila Alem ; Kalina YacefSource :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 1998.
English descriptors
- KwdEn :
- Adaptive, Adaptive instruction, Ateeg, Blackboard architecture, Cfost, Curriculum agent, Curriculum formalism, Curriculum planning, Domain expert, Domain expert agent, Effective performance, Elementary skills, Exercise base, Filip, Filip framework, Generic model, Great hope, Instruction delivery, Instructional, Instructional method, Instructional methods, Instructor agent, Learner, Learner agent, Learner model, Major tasks, Operational skill training, Performance measures, Reasoning rules, Simulator, Situation characteristics, Skill development, Skill development agent, Support operators performance, Traffic situations, Training exercise, Training exercises, Training goals, Training progress, Training situation characteristics, Training situations, Training system, Training topics, User interface, Workload.
- Teeft :
- Adaptive, Adaptive instruction, Ateeg, Blackboard architecture, Cfost, Curriculum agent, Curriculum formalism, Curriculum planning, Domain expert, Domain expert agent, Effective performance, Elementary skills, Exercise base, Filip, Filip framework, Generic model, Great hope, Instruction delivery, Instructional, Instructional method, Instructional methods, Instructor agent, Learner, Learner agent, Learner model, Major tasks, Operational skill training, Performance measures, Reasoning rules, Simulator, Situation characteristics, Skill development, Skill development agent, Support operators performance, Traffic situations, Training exercise, Training exercises, Training goals, Training progress, Training situation characteristics, Training situations, Training system, Training topics, User interface, Workload.
Abstract
Abstract: This paper presents FILIP, a multi-agent framework for the design of Intelligent Simulation-based Learning Environment (ISLE). Such a learning environment assesses the learner and provides adaptive instruction when the learner is developing his/her operational skill in dynamic and highly risky domains. The FILIP framework offers a great hope as a means of helping learners develop the skill necessary for effective performance. It is geared towards skill development and acquisition in order to develop and support operators performance. FILIP is designed as a multi agent architecture, which includes the seven agents: a simulator; a user interface; a domain expert agent; a learner agent; an instructor agent, a curriculum agent and a skill development agent. FILIP framework is distinguished from other traditional ISLE by its skill development agent and the curriculum agent. The skill development agent is to advise the instructor on issues related to the skill development of the learner so that it can be taken into account in the planning of the instruction. The curriculum agent is made of a Curriculum Formalism for Operational Skill Training (CFOST). It provides information for skill development agent to assess skill development and for the instructor agent to provide adaptive instruction. As an application of FILIP in the context of Air Traffic Control (ATC) training, ATEEG is described. It adopts a blackboard architecture based on a multi-agent approach, in which major tasks of the training are distributed into agents. The architecture of ATEEG is composed of an air traffic control simulator; a domain expert called here the ideal ATC which represents an ATC expert and provides expert's problem solving strategies and solutions to given traffic situations presented to the learner; a learner which gathers the learner's actions during the execution of a training exercise and derives a bi-dimension learner model; an instructor which adapts instruction to the individual learner's needs at two levels: tailoring training exercises and adapting instructional methods; an ATC curriculum, which provides a hierarchical network of ATC training topics, training situations and instructional methods; and an ATC exercise base that represents existing ATC training exercises in terms of underlying training topics presented, complexity of training situations. An object sever facilities the data flow and communication among ATEEG agents.
Url:
DOI: 10.1007/BFb0055031
Links to Exploration step
ISTEX:F9F02040D0C0B2DE5A988C2339CA5F7F43CDE513Le document en format XML
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sort="Yacef, Kalina" uniqKey="Yacef K" first="Kalina" last="Yacef">Kalina Yacef</name><affiliation><mods:affiliation>Thomson Radar Australia Corporation and Laboratoire d'Intelligence Artificielle de Paris 5, France</mods:affiliation></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">ISTEX</idno><idno type="RBID">ISTEX:F9F02040D0C0B2DE5A988C2339CA5F7F43CDE513</idno><date when="1998" year="1998">1998</date><idno type="doi">10.1007/BFb0055031</idno><idno type="url">https://api.istex.fr/document/F9F02040D0C0B2DE5A988C2339CA5F7F43CDE513/fulltext/pdf</idno><idno type="wicri:Area/Istex/Corpus">002F10</idno><idno type="wicri:explorRef" wicri:stream="Istex" wicri:step="Corpus" wicri:corpus="ISTEX">002F10</idno></publicationStmt><sourceDesc><biblStruct><analytic><title level="a" type="main" xml:lang="en">Using multi-agent approach for the design of an intelligent learning environment</title><author><name sortKey="Zhang, Dong Mei" sort="Zhang, Dong Mei" uniqKey="Zhang D" first="Dong Mei" last="Zhang">Dong Mei Zhang</name><affiliation><mods:affiliation>CSIRO Mathematical and Information Sciences, Locked bag 17, 2113, North Ryde, NSW, Australia</mods:affiliation></affiliation><affiliation><mods:affiliation>E-mail: dongzhang@cmis.csiro.au</mods:affiliation></affiliation></author><author><name sortKey="Alem, Leila" sort="Alem, Leila" uniqKey="Alem L" first="Leila" last="Alem">Leila Alem</name><affiliation><mods:affiliation>CSIRO Mathematical and Information Sciences, Locked bag 17, 2113, North Ryde, NSW, Australia</mods:affiliation></affiliation><affiliation><mods:affiliation>E-mail: leila.alem@cmis.csiroau</mods:affiliation></affiliation></author><author><name sortKey="Yacef, Kalina" sort="Yacef, Kalina" uniqKey="Yacef K" first="Kalina" last="Yacef">Kalina Yacef</name><affiliation><mods:affiliation>Thomson Radar Australia Corporation and Laboratoire d'Intelligence Artificielle de Paris 5, 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method</term><term>Instructional methods</term><term>Instructor agent</term><term>Learner</term><term>Learner agent</term><term>Learner model</term><term>Major tasks</term><term>Operational skill training</term><term>Performance measures</term><term>Reasoning rules</term><term>Simulator</term><term>Situation characteristics</term><term>Skill development</term><term>Skill development agent</term><term>Support operators performance</term><term>Traffic situations</term><term>Training exercise</term><term>Training exercises</term><term>Training goals</term><term>Training progress</term><term>Training situation characteristics</term><term>Training situations</term><term>Training system</term><term>Training topics</term><term>User interface</term><term>Workload</term></keywords><keywords scheme="Teeft" xml:lang="en"><term>Adaptive</term><term>Adaptive instruction</term><term>Ateeg</term><term>Blackboard architecture</term><term>Cfost</term><term>Curriculum agent</term><term>Curriculum formalism</term><term>Curriculum planning</term><term>Domain expert</term><term>Domain expert agent</term><term>Effective performance</term><term>Elementary skills</term><term>Exercise base</term><term>Filip</term><term>Filip framework</term><term>Generic model</term><term>Great hope</term><term>Instruction delivery</term><term>Instructional</term><term>Instructional method</term><term>Instructional methods</term><term>Instructor agent</term><term>Learner</term><term>Learner agent</term><term>Learner model</term><term>Major tasks</term><term>Operational skill training</term><term>Performance measures</term><term>Reasoning rules</term><term>Simulator</term><term>Situation characteristics</term><term>Skill development</term><term>Skill development agent</term><term>Support operators performance</term><term>Traffic situations</term><term>Training exercise</term><term>Training exercises</term><term>Training goals</term><term>Training progress</term><term>Training situation characteristics</term><term>Training situations</term><term>Training system</term><term>Training topics</term><term>User interface</term><term>Workload</term></keywords></textClass><langUsage><language ident="en">en</language></langUsage></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Abstract: This paper presents FILIP, a multi-agent framework for the design of Intelligent Simulation-based Learning Environment (ISLE). Such a learning environment assesses the learner and provides adaptive instruction when the learner is developing his/her operational skill in dynamic and highly risky domains. The FILIP framework offers a great hope as a means of helping learners develop the skill necessary for effective performance. It is geared towards skill development and acquisition in order to develop and support operators performance. FILIP is designed as a multi agent architecture, which includes the seven agents: a simulator; a user interface; a domain expert agent; a learner agent; an instructor agent, a curriculum agent and a skill development agent. FILIP framework is distinguished from other traditional ISLE by its skill development agent and the curriculum agent. The skill development agent is to advise the instructor on issues related to the skill development of the learner so that it can be taken into account in the planning of the instruction. The curriculum agent is made of a Curriculum Formalism for Operational Skill Training (CFOST). It provides information for skill development agent to assess skill development and for the instructor agent to provide adaptive instruction. As an application of FILIP in the context of Air Traffic Control (ATC) training, ATEEG is described. It adopts a blackboard architecture based on a multi-agent approach, in which major tasks of the training are distributed into agents. The architecture of ATEEG is composed of an air traffic control simulator; a domain expert called here the ideal ATC which represents an ATC expert and provides expert's problem solving strategies and solutions to given traffic situations presented to the learner; a learner which gathers the learner's actions during the execution of a training exercise and derives a bi-dimension learner model; an instructor which adapts instruction to the individual learner's needs at two levels: tailoring training exercises and adapting instructional methods; an ATC curriculum, which provides a hierarchical network of ATC training topics, training situations and instructional methods; and an ATC exercise base that represents existing ATC training exercises in terms of underlying training topics presented, complexity of training situations. An object sever facilities the data flow and communication among ATEEG agents.</div></front></TEI><istex><corpusName>springer</corpusName><keywords><teeft><json:string>learner</json:string><json:string>ateeg</json:string><json:string>filip</json:string><json:string>learner agent</json:string><json:string>training goals</json:string><json:string>instructor agent</json:string><json:string>learner model</json:string><json:string>skill development agent</json:string><json:string>training topics</json:string><json:string>training situations</json:string><json:string>instructional methods</json:string><json:string>curriculum agent</json:string><json:string>simulator</json:string><json:string>training exercises</json:string><json:string>workload</json:string><json:string>cfost</json:string><json:string>adaptive</json:string><json:string>skill development</json:string><json:string>instructional method</json:string><json:string>training exercise</json:string><json:string>training situation characteristics</json:string><json:string>curriculum formalism</json:string><json:string>operational skill training</json:string><json:string>filip framework</json:string><json:string>adaptive instruction</json:string><json:string>curriculum planning</json:string><json:string>performance measures</json:string><json:string>elementary skills</json:string><json:string>exercise base</json:string><json:string>domain expert agent</json:string><json:string>instruction delivery</json:string><json:string>traffic situations</json:string><json:string>training system</json:string><json:string>effective performance</json:string><json:string>reasoning rules</json:string><json:string>situation characteristics</json:string><json:string>support operators performance</json:string><json:string>generic model</json:string><json:string>great hope</json:string><json:string>training progress</json:string><json:string>blackboard architecture</json:string><json:string>major tasks</json:string><json:string>user interface</json:string><json:string>domain expert</json:string><json:string>instructional</json:string></teeft></keywords><author><json:item><name>Dong Mei Zhang</name><affiliations><json:string>CSIRO Mathematical and Information Sciences, Locked bag 17, 2113, North Ryde, NSW, Australia</json:string><json:string>E-mail: dongzhang@cmis.csiro.au</json:string></affiliations></json:item><json:item><name>Leila Alem</name><affiliations><json:string>CSIRO Mathematical and Information Sciences, Locked bag 17, 2113, North Ryde, NSW, Australia</json:string><json:string>E-mail: leila.alem@cmis.csiroau</json:string></affiliations></json:item><json:item><name>Kalina Yacef</name><affiliations><json:string>Thomson Radar Australia Corporation and Laboratoire d'Intelligence Artificielle de Paris 5, France</json:string></affiliations></json:item></author><arkIstex>ark:/67375/1BB-PTL43650-C</arkIstex><language><json:string>eng</json:string></language><originalGenre><json:string>ReviewPaper</json:string></originalGenre><abstract>Abstract: This paper presents FILIP, a multi-agent framework for the design of Intelligent Simulation-based Learning Environment (ISLE). Such a learning environment assesses the learner and provides adaptive instruction when the learner is developing his/her operational skill in dynamic and highly risky domains. The FILIP framework offers a great hope as a means of helping learners develop the skill necessary for effective performance. It is geared towards skill development and acquisition in order to develop and support operators performance. FILIP is designed as a multi agent architecture, which includes the seven agents: a simulator; a user interface; a domain expert agent; a learner agent; an instructor agent, a curriculum agent and a skill development agent. FILIP framework is distinguished from other traditional ISLE by its skill development agent and the curriculum agent. The skill development agent is to advise the instructor on issues related to the skill development of the learner so that it can be taken into account in the planning of the instruction. The curriculum agent is made of a Curriculum Formalism for Operational Skill Training (CFOST). It provides information for skill development agent to assess skill development and for the instructor agent to provide adaptive instruction. As an application of FILIP in the context of Air Traffic Control (ATC) training, ATEEG is described. It adopts a blackboard architecture based on a multi-agent approach, in which major tasks of the training are distributed into agents. The architecture of ATEEG is composed of an air traffic control simulator; a domain expert called here the ideal ATC which represents an ATC expert and provides expert's problem solving strategies and solutions to given traffic situations presented to the learner; a learner which gathers the learner's actions during the execution of a training exercise and derives a bi-dimension learner model; an instructor which adapts instruction to the individual learner's needs at two levels: tailoring training exercises and adapting instructional methods; an ATC curriculum, which provides a hierarchical network of ATC training topics, training situations and instructional methods; and an ATC exercise base that represents existing ATC training exercises in terms of underlying training topics presented, complexity of training situations. An object sever facilities the data flow and communication among ATEEG agents.</abstract><qualityIndicators><refBibsNative>false</refBibsNative><abstractWordCount>368</abstractWordCount><abstractCharCount>2466</abstractCharCount><keywordCount>0</keywordCount><score>8.483</score><pdfWordCount>3483</pdfWordCount><pdfCharCount>22002</pdfCharCount><pdfVersion>1.3</pdfVersion><pdfPageCount>11</pdfPageCount><pdfPageSize>440.64 x 666 pts</pdfPageSize></qualityIndicators><title>Using multi-agent approach for the design of an intelligent learning environment</title><chapterId><json:string>16</json:string><json:string>Chap16</json:string></chapterId><genre><json:string>conference</json:string></genre><serie><title>Lecture Notes in Computer Science</title><language><json:string>unknown</json:string></language><copyrightDate>1998</copyrightDate><issn><json:string>0302-9743</json:string></issn><eissn><json:string>1611-3349</json:string></eissn><editor><json:item><name>Jaime G. Carbonell</name></json:item><json:item><name>Jöorg Siekmann</name></json:item><json:item><name>G. Goos</name></json:item><json:item><name>J. Hartmanis</name></json:item><json:item><name>J. van Leeuwen</name></json:item></editor></serie><host><title>Agents and Multi-Agent Systems Formalisms, Methodologies, and Applications</title><language><json:string>unknown</json:string></language><copyrightDate>1998</copyrightDate><doi><json:string>10.1007/BFb0055015</json:string></doi><issn><json:string>0302-9743</json:string></issn><eissn><json:string>1611-3349</json:string></eissn><eisbn><json:string>978-3-540-68722-1</json:string></eisbn><bookId><json:string>3540647694</json:string></bookId><isbn><json:string>978-3-540-64769-0</json:string></isbn><volume>1441</volume><pages><first>220</first><last>230</last></pages><genre><json:string>book-series</json:string></genre><editor><json:item><name>Wayne Wobcke</name></json:item><json:item><name>Maurice Pagnucco</name></json:item><json:item><name>Chengqi Zhang</name></json:item></editor><subject><json:item><value>Computer Science</value></json:item><json:item><value>Computer Science</value></json:item><json:item><value>Artificial Intelligence (incl. Robotics)</value></json:item><json:item><value>Computer Communication Networks</value></json:item><json:item><value>Software Engineering</value></json:item></subject></host><namedEntities><unitex><date><json:string>1998</json:string></date><geogName></geogName><orgName><json:string>In Intelligent Tutoring Systems</json:string><json:string>Intelligent Tutoring Systems</json:string></orgName><orgName_funder></orgName_funder><orgName_provider></orgName_provider><persName><json:string>C. Frasson</json:string><json:string>A. Munro</json:string><json:string>View</json:string><json:string>K. Yacef</json:string><json:string>G. Gauthier</json:string><json:string>E. Costa</json:string><json:string>T. Mengelle</json:string><json:string>W. van Joolingen</json:string><json:string>Information</json:string><json:string>Mei Zhang</json:string><json:string>A. Perkusich</json:string><json:string>Architecture</json:string><json:string>Kalina Yacef</json:string><json:string>Leila Alem</json:string><json:string>G. Gouarderes</json:string><json:string>A. Lesgold</json:string><json:string>Agent</json:string><json:string>D. Zhang</json:string><json:string>B.P. Woolf</json:string><json:string>L. Alem</json:string><json:string>E. Aimeur</json:string></persName><placeName><json:string>Montreal</json:string><json:string>Canada</json:string><json:string>Japan</json:string><json:string>Kobe</json:string></placeName><ref_url></ref_url><ref_bibl><json:string>[6]</json:string><json:string>[1]</json:string><json:string>[3]</json:string><json:string>[5]</json:string></ref_bibl><bibl></bibl></unitex></namedEntities><ark><json:string>ark:/67375/1BB-PTL43650-C</json:string></ark><categories><inist><json:string>1 - sciences humaines et 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GROBID</resp><name resp="ISTEX-API">ISTEX-API (INIST-CNRS)</name></respStmt></titleStmt><publicationStmt><authority>ISTEX</authority><publisher scheme="https://publisher-list.data.istex.fr">Springer Berlin Heidelberg</publisher><pubPlace>Berlin, Heidelberg</pubPlace><availability><licence><p>Springer-Verlag, 1998</p></licence><p scheme="https://loaded-corpus.data.istex.fr/ark:/67375/XBH-3XSW68JL-F">springer</p></availability><date>1998</date></publicationStmt><notesStmt><note type="conference" scheme="https://content-type.data.istex.fr/ark:/67375/XTP-BFHXPBJJ-3">conference</note><note type="book-series" scheme="https://publication-type.data.istex.fr/ark:/67375/JMC-0G6R5W5T-Z">book-series</note></notesStmt><sourceDesc><biblStruct type="inbook"><analytic><title level="a" type="main" xml:lang="en">Using multi-agent approach for the design of an intelligent learning environment</title><author xml:id="author-0000"><persName><forename type="first">Dong</forename><surname>Zhang</surname></persName><email>dongzhang@cmis.csiro.au</email><affiliation>CSIRO Mathematical and Information Sciences, Locked bag 17, 2113, North Ryde, NSW, Australia</affiliation></author><author xml:id="author-0001"><persName><forename type="first">Leila</forename><surname>Alem</surname></persName><email>leila.alem@cmis.csiroau</email><affiliation>CSIRO Mathematical and Information Sciences, Locked bag 17, 2113, North Ryde, NSW, Australia</affiliation></author><author xml:id="author-0002"><persName><forename type="first">Kalina</forename><surname>Yacef</surname></persName><affiliation>Thomson Radar Australia Corporation and Laboratoire d'Intelligence Artificielle de Paris 5, France</affiliation></author><idno type="istex">F9F02040D0C0B2DE5A988C2339CA5F7F43CDE513</idno><idno type="ark">ark:/67375/1BB-PTL43650-C</idno><idno type="DOI">10.1007/BFb0055031</idno><idno type="ChapterID">16</idno><idno type="ChapterID">Chap16</idno></analytic><monogr><title level="m">Agents and Multi-Agent Systems Formalisms, Methodologies, and Applications</title><title level="m" type="sub">Based on the AI'97 Workshop on Commonsense Reasoning, Intelligent Agents, and Distributed Artificial Intelligence Perth, Australia, December 1, 1997</title><idno type="DOI">10.1007/BFb0055015</idno><idno type="pISBN">978-3-540-64769-0</idno><idno type="eISBN">978-3-540-68722-1</idno><idno type="pISSN">0302-9743</idno><idno type="eISSN">1611-3349</idno><idno type="book-title-id">55355</idno><idno type="book-id">3540647694</idno><idno type="book-chapter-count">17</idno><idno type="book-volume-number">1441</idno><editor xml:id="book-author-0000"><persName><forename type="first">Wayne</forename><surname>Wobcke</surname></persName></editor><editor xml:id="book-author-0001"><persName><forename type="first">Maurice</forename><surname>Pagnucco</surname></persName></editor><editor xml:id="book-author-0002"><persName><forename type="first">Chengqi</forename><surname>Zhang</surname></persName></editor><imprint><publisher>Springer Berlin Heidelberg</publisher><pubPlace>Berlin, Heidelberg</pubPlace><date type="published" when="2006-05-24"></date><biblScope unit="volume">1441</biblScope><biblScope unit="page" from="220">220</biblScope><biblScope unit="page" to="230">230</biblScope></imprint></monogr><series><title level="s">Lecture Notes in Computer Science</title><editor xml:id="serie-author-0000"><persName><forename type="first">Jaime</forename><forename type="first">G.</forename><surname>Carbonell</surname></persName></editor><editor xml:id="serie-author-0001"><persName><forename type="first">Jöorg</forename><surname>Siekmann</surname></persName></editor><editor xml:id="serie-author-0002"><persName><forename type="first">G.</forename><surname>Goos</surname></persName></editor><editor xml:id="serie-author-0003"><persName><forename type="first">J.</forename><surname>Hartmanis</surname></persName></editor><editor xml:id="serie-author-0004"><persName><forename type="first">J.</forename><surname>van Leeuwen</surname></persName></editor><biblScope><date>1998</date></biblScope><idno type="pISSN">0302-9743</idno><idno type="eISSN">1611-3349</idno><idno type="series-id">558</idno></series></biblStruct></sourceDesc></fileDesc><profileDesc><creation><date>1998</date></creation><langUsage><language ident="en">en</language></langUsage><abstract xml:lang="en"><p>Abstract: This paper presents FILIP, a multi-agent framework for the design of Intelligent Simulation-based Learning Environment (ISLE). Such a learning environment assesses the learner and provides adaptive instruction when the learner is developing his/her operational skill in dynamic and highly risky domains. The FILIP framework offers a great hope as a means of helping learners develop the skill necessary for effective performance. It is geared towards skill development and acquisition in order to develop and support operators performance. FILIP is designed as a multi agent architecture, which includes the seven agents: a simulator; a user interface; a domain expert agent; a learner agent; an instructor agent, a curriculum agent and a skill development agent. FILIP framework is distinguished from other traditional ISLE by its skill development agent and the curriculum agent. The skill development agent is to advise the instructor on issues related to the skill development of the learner so that it can be taken into account in the planning of the instruction. The curriculum agent is made of a Curriculum Formalism for Operational Skill Training (CFOST). It provides information for skill development agent to assess skill development and for the instructor agent to provide adaptive instruction. As an application of FILIP in the context of Air Traffic Control (ATC) training, ATEEG is described. It adopts a blackboard architecture based on a multi-agent approach, in which major tasks of the training are distributed into agents. The architecture of ATEEG is composed of an air traffic control simulator; a domain expert called here the ideal ATC which represents an ATC expert and provides expert's problem solving strategies and solutions to given traffic situations presented to the learner; a learner which gathers the learner's actions during the execution of a training exercise and derives a bi-dimension learner model; an instructor which adapts instruction to the individual learner's needs at two levels: tailoring training exercises and adapting instructional methods; an ATC curriculum, which provides a hierarchical network of ATC training topics, training situations and instructional methods; and an ATC exercise base that represents existing ATC training exercises in terms of underlying training topics presented, complexity of training situations. An object sever facilities the data flow and communication among ATEEG agents.</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>I13022</label><label>I14029</label><item><term>Computer Science</term></item><item><term>Artificial Intelligence (incl. Robotics)</term></item><item><term>Computer Communication Networks</term></item><item><term>Software Engineering</term></item></list></keywords></textClass></profileDesc><revisionDesc><change when="2006-05-24">Published</change><change xml:id="refBibs-istex" who="#ISTEX-API" when="2017-10-3">References added</change></revisionDesc></teiHeader></istex:fulltextTEI><json:item><extension>txt</extension><original>false</original><mimetype>text/plain</mimetype><uri>https://api.istex.fr/document/F9F02040D0C0B2DE5A988C2339CA5F7F43CDE513/fulltext/txt</uri></json:item></fulltext><metadata><istex:metadataXml wicri:clean="Springer, Publisher found" wicri:toSee="no header"><istex:xmlDeclaration>version="1.0" encoding="UTF-8"</istex:xmlDeclaration><istex:docType PUBLIC="-//Springer-Verlag//DTD A++ V2.4//EN" URI="http://devel.springer.de/A++/V2.4/DTD/A++V2.4.dtd" name="istex:docType"></istex:docType><istex:document><Publisher><PublisherInfo><PublisherName>Springer Berlin Heidelberg</PublisherName><PublisherLocation>Berlin, Heidelberg</PublisherLocation></PublisherInfo><Series><SeriesInfo TocLevels="0"><SeriesID>558</SeriesID><SeriesPrintISSN>0302-9743</SeriesPrintISSN><SeriesElectronicISSN>1611-3349</SeriesElectronicISSN><SeriesTitle Language="En">Lecture Notes in Computer Science</SeriesTitle><SeriesAbbreviatedTitle>Lect Notes Comput Sci</SeriesAbbreviatedTitle></SeriesInfo><SeriesHeader><EditorGroup><Editor><EditorName DisplayOrder="Western"><GivenName>Jaime</GivenName><GivenName>G.</GivenName><FamilyName>Carbonell</FamilyName></EditorName></Editor><Editor><EditorName DisplayOrder="Western"><GivenName>Jöorg</GivenName><FamilyName>Siekmann</FamilyName></EditorName></Editor><Editor><EditorName DisplayOrder="Western"><GivenName>G.</GivenName><FamilyName>Goos</FamilyName></EditorName></Editor><Editor><EditorName DisplayOrder="Western"><GivenName>J.</GivenName><FamilyName>Hartmanis</FamilyName></EditorName></Editor><Editor><EditorName DisplayOrder="Western"><GivenName>J.</GivenName><Particle>van</Particle><FamilyName>Leeuwen</FamilyName></EditorName></Editor></EditorGroup></SeriesHeader><Book Language="En"><BookInfo MediaType="eBook" Language="En" BookProductType="Proceedings" TocLevels="0" NumberingStyle="Unnumbered"><BookID>3540647694</BookID><BookTitle>Agents and Multi-Agent Systems Formalisms, Methodologies, and Applications</BookTitle><BookSubTitle>Based on the AI'97 Workshop on Commonsense Reasoning, Intelligent Agents, and Distributed Artificial Intelligence Perth, Australia, December 1, 1997</BookSubTitle><BookVolumeNumber>1441</BookVolumeNumber><BookDOI>10.1007/BFb0055015</BookDOI><BookTitleID>55355</BookTitleID><BookPrintISBN>978-3-540-64769-0</BookPrintISBN><BookElectronicISBN>978-3-540-68722-1</BookElectronicISBN><BookChapterCount>17</BookChapterCount><BookCopyright><CopyrightHolderName>Springer-Verlag</CopyrightHolderName><CopyrightYear>1998</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><SubjectCollection Code="SUCO11645">Computer Science</SubjectCollection></BookSubjectGroup></BookInfo><BookHeader><EditorGroup><Editor><EditorName DisplayOrder="Western"><GivenName>Wayne</GivenName><FamilyName>Wobcke</FamilyName></EditorName></Editor><Editor><EditorName DisplayOrder="Western"><GivenName>Maurice</GivenName><FamilyName>Pagnucco</FamilyName></EditorName></Editor><Editor><EditorName DisplayOrder="Western"><GivenName>Chengqi</GivenName><FamilyName>Zhang</FamilyName></EditorName></Editor></EditorGroup></BookHeader><Chapter ID="Chap16" Language="En"><ChapterInfo ChapterType="ReviewPaper" NumberingStyle="Unnumbered" TocLevels="0" ContainsESM="No"><ChapterID>16</ChapterID><ChapterDOI>10.1007/BFb0055031</ChapterDOI><ChapterSequenceNumber>16</ChapterSequenceNumber><ChapterTitle Language="En">Using multi-agent approach for the design of an intelligent learning environment</ChapterTitle><ChapterFirstPage>220</ChapterFirstPage><ChapterLastPage>230</ChapterLastPage><ChapterCopyright><CopyrightHolderName>Springer-Verlag</CopyrightHolderName><CopyrightYear>1998</CopyrightYear></ChapterCopyright><ChapterHistory><OnlineDate><Year>2006</Year><Month>5</Month><Day>24</Day></OnlineDate></ChapterHistory><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>3540647694</BookID><BookTitle>Agents and Multi-Agent Systems Formalisms, Methodologies, and Applications</BookTitle></ChapterContext></ChapterInfo><ChapterHeader><AuthorGroup><Author AffiliationIDS="Aff1"><AuthorName DisplayOrder="Western"><GivenName>Dong</GivenName><GivenName>Mei</GivenName><FamilyName>Zhang</FamilyName></AuthorName><Contact><Fax>61 2 93253200</Fax><Email>dongzhang@cmis.csiro.au</Email></Contact></Author><Author AffiliationIDS="Aff1"><AuthorName DisplayOrder="Western"><GivenName>Leila</GivenName><FamilyName>Alem</FamilyName></AuthorName><Contact><Email>leila.alem@cmis.csiroau</Email></Contact></Author><Author AffiliationIDS="Aff2"><AuthorName DisplayOrder="Western"><GivenName>Kalina</GivenName><FamilyName>Yacef</FamilyName></AuthorName></Author><Affiliation ID="Aff1"><OrgName>CSIRO Mathematical and Information Sciences</OrgName><OrgAddress><Postbox>Locked bag 17</Postbox><Postcode>2113</Postcode><City>North Ryde</City><State>NSW</State><Country>Australia</Country></OrgAddress></Affiliation><Affiliation ID="Aff2"><OrgName>Thomson Radar Australia Corporation and Laboratoire d'Intelligence Artificielle de Paris 5</OrgName><OrgAddress><Country>France</Country></OrgAddress></Affiliation></AuthorGroup><Abstract ID="Abs1" Language="En"><Heading>Abstract</Heading><Para>This paper presents FILIP, a multi-agent framework for the design of Intelligent Simulation-based Learning Environment (ISLE). Such a learning environment assesses the learner and provides adaptive instruction when the learner is developing his/her operational skill in dynamic and highly risky domains. The FILIP framework offers a great hope as a means of helping learners develop the skill necessary for effective performance. It is geared towards skill development and acquisition in order to develop and support operators performance. FILIP is designed as a multi agent architecture, which includes the seven agents: a simulator; a user interface; a domain expert agent; a learner agent; an instructor agent, a curriculum agent and a skill development agent. FILIP framework is distinguished from other traditional ISLE by its skill development agent and the curriculum agent. The skill development agent is to advise the instructor on issues related to the skill development of the learner so that it can be taken into account in the planning of the instruction. The curriculum agent is made of a Curriculum Formalism for Operational Skill Training (CFOST). It provides information for skill development agent to assess skill development and for the instructor agent to provide adaptive instruction.</Para><Para>As an application of FILIP in the context of Air Traffic Control (ATC) training, ATEEG is described. It adopts a blackboard architecture based on a multi-agent approach, in which major tasks of the training are distributed into agents. The architecture of ATEEG is composed of an air traffic control simulator; a domain expert called here the <Emphasis Type="Italic">ideal ATC</Emphasis> which represents an ATC expert and provides expert's problem solving strategies and solutions to given traffic situations presented to the learner; a <Emphasis Type="Italic">learner</Emphasis> which gathers the learner's actions during the execution of a training exercise and derives a bi-dimension learner model; an <Emphasis Type="Italic">instructor</Emphasis> which adapts instruction to the individual learner's needs at two levels: tailoring training exercises and adapting instructional methods; an <Emphasis Type="Italic">ATC curriculum</Emphasis>, which provides a hierarchical network of ATC training topics, training situations and instructional methods; and an ATC <Emphasis Type="Italic">exercise base</Emphasis> that represents existing ATC training exercises in terms of underlying training topics presented, complexity of training situations. An object sever facilities the data flow and communication among ATEEG agents.</Para></Abstract><KeywordGroup Language="En"><Heading>Keywords</Heading><Keyword>multi-agent training system</Keyword><Keyword>agent modelling</Keyword><Keyword>intelligent tutoring system</Keyword><Keyword>simulation-based learning</Keyword></KeywordGroup></ChapterHeader><NoBody></NoBody></Chapter></Book></Series></Publisher></istex:document></istex:metadataXml><mods version="3.6"><titleInfo lang="en"><title>Using multi-agent approach for the design of an intelligent learning environment</title></titleInfo><titleInfo type="alternative" contentType="CDATA" lang="en"><title>Using multi-agent approach for the design of an intelligent learning environment</title></titleInfo><name type="personal"><namePart type="given">Dong</namePart><namePart type="given">Mei</namePart><namePart type="family">Zhang</namePart><affiliation>CSIRO Mathematical and Information Sciences, Locked bag 17, 2113, North Ryde, NSW, Australia</affiliation><affiliation>E-mail: dongzhang@cmis.csiro.au</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Leila</namePart><namePart type="family">Alem</namePart><affiliation>CSIRO Mathematical and Information Sciences, Locked bag 17, 2113, North Ryde, NSW, Australia</affiliation><affiliation>E-mail: leila.alem@cmis.csiroau</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Kalina</namePart><namePart type="family">Yacef</namePart><affiliation>Thomson Radar Australia Corporation and Laboratoire d'Intelligence Artificielle de Paris 5, France</affiliation><role><roleTerm type="text">author</roleTerm></role></name><typeOfResource>text</typeOfResource><genre displayLabel="ReviewPaper" authority="ISTEX" authorityURI="https://content-type.data.istex.fr" type="conference" valueURI="https://content-type.data.istex.fr/ark:/67375/XTP-BFHXPBJJ-3">conference</genre><originInfo><publisher>Springer Berlin Heidelberg</publisher><place><placeTerm type="text">Berlin, Heidelberg</placeTerm></place><dateIssued encoding="w3cdtf">2006-05-24</dateIssued><copyrightDate encoding="w3cdtf">1998</copyrightDate></originInfo><language><languageTerm type="code" authority="rfc3066">en</languageTerm><languageTerm type="code" authority="iso639-2b">eng</languageTerm></language><abstract lang="en">Abstract: This paper presents FILIP, a multi-agent framework for the design of Intelligent Simulation-based Learning Environment (ISLE). Such a learning environment assesses the learner and provides adaptive instruction when the learner is developing his/her operational skill in dynamic and highly risky domains. The FILIP framework offers a great hope as a means of helping learners develop the skill necessary for effective performance. It is geared towards skill development and acquisition in order to develop and support operators performance. FILIP is designed as a multi agent architecture, which includes the seven agents: a simulator; a user interface; a domain expert agent; a learner agent; an instructor agent, a curriculum agent and a skill development agent. FILIP framework is distinguished from other traditional ISLE by its skill development agent and the curriculum agent. The skill development agent is to advise the instructor on issues related to the skill development of the learner so that it can be taken into account in the planning of the instruction. The curriculum agent is made of a Curriculum Formalism for Operational Skill Training (CFOST). It provides information for skill development agent to assess skill development and for the instructor agent to provide adaptive instruction. As an application of FILIP in the context of Air Traffic Control (ATC) training, ATEEG is described. It adopts a blackboard architecture based on a multi-agent approach, in which major tasks of the training are distributed into agents. The architecture of ATEEG is composed of an air traffic control simulator; a domain expert called here the ideal ATC which represents an ATC expert and provides expert's problem solving strategies and solutions to given traffic situations presented to the learner; a learner which gathers the learner's actions during the execution of a training exercise and derives a bi-dimension learner model; an instructor which adapts instruction to the individual learner's needs at two levels: tailoring training exercises and adapting instructional methods; an ATC curriculum, which provides a hierarchical network of ATC training topics, training situations and instructional methods; and an ATC exercise base that represents existing ATC training exercises in terms of underlying training topics presented, complexity of training situations. 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Robotics)</topic><topic authority="SpringerSubjectCodes" authorityURI="I13022">Computer Communication Networks</topic><topic authority="SpringerSubjectCodes" authorityURI="I14029">Software Engineering</topic></subject><identifier type="DOI">10.1007/BFb0055015</identifier><identifier type="ISBN">978-3-540-64769-0</identifier><identifier type="eISBN">978-3-540-68722-1</identifier><identifier type="ISSN">0302-9743</identifier><identifier type="eISSN">1611-3349</identifier><identifier type="BookTitleID">55355</identifier><identifier type="BookID">3540647694</identifier><identifier type="BookChapterCount">17</identifier><identifier type="BookVolumeNumber">1441</identifier><part><date>1998</date><detail type="volume"><number>1441</number><caption>vol.</caption></detail><extent unit="pages"><start>220</start><end>230</end></extent></part><recordInfo><recordOrigin>Springer-Verlag, 1998</recordOrigin></recordInfo></relatedItem><relatedItem type="series"><titleInfo><title>Lecture Notes in Computer Science</title></titleInfo><name type="personal"><namePart type="given">Jaime</namePart><namePart type="given">G.</namePart><namePart type="family">Carbonell</namePart><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Jöorg</namePart><namePart type="family">Siekmann</namePart><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">G.</namePart><namePart type="family">Goos</namePart><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">J.</namePart><namePart type="family">Hartmanis</namePart><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">J.</namePart><namePart type="family">van Leeuwen</namePart><role><roleTerm type="text">editor</roleTerm></role></name><originInfo><publisher>Springer</publisher><copyrightDate encoding="w3cdtf">1998</copyrightDate><issuance>serial</issuance></originInfo><identifier type="ISSN">0302-9743</identifier><identifier type="eISSN">1611-3349</identifier><identifier type="SeriesID">558</identifier><recordInfo><recordOrigin>Springer-Verlag, 1998</recordOrigin></recordInfo></relatedItem><identifier type="istex">F9F02040D0C0B2DE5A988C2339CA5F7F43CDE513</identifier><identifier type="ark">ark:/67375/1BB-PTL43650-C</identifier><identifier type="DOI">10.1007/BFb0055031</identifier><identifier type="ChapterID">16</identifier><identifier type="ChapterID">Chap16</identifier><accessCondition type="use and reproduction" contentType="copyright">Springer-Verlag, 1998</accessCondition><recordInfo><recordContentSource authority="ISTEX" authorityURI="https://loaded-corpus.data.istex.fr" valueURI="https://loaded-corpus.data.istex.fr/ark:/67375/XBH-3XSW68JL-F">springer</recordContentSource><recordOrigin>Springer-Verlag, 1998</recordOrigin></recordInfo></mods><json:item><extension>json</extension><original>false</original><mimetype>application/json</mimetype><uri>https://api.istex.fr/document/F9F02040D0C0B2DE5A988C2339CA5F7F43CDE513/metadata/json</uri></json:item></metadata></istex></record>Pour manipuler ce document sous Unix (Dilib)
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