Using multi-agent approach for the design of an intelligent learning environment
Identifieur interne : 002F10 ( Istex/Curation ); précédent : 002F09; suivant : 002F11Using multi-agent approach for the design of an intelligent learning environment
Auteurs : Dong Mei Zhang [Australie] ; Leila Alem [Australie] ; Kalina Yacef [France]Source :
- 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
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and Information Sciences, Locked bag 17, 2113, North Ryde, NSW, Australia</mods:affiliation><country xml:lang="fr">Australie</country><wicri:regionArea>CSIRO Mathematical and Information Sciences, Locked bag 17, 2113, North Ryde, NSW</wicri:regionArea></affiliation><affiliation><mods:affiliation>E-mail: leila.alem@cmis.csiroau</mods:affiliation><wicri:noCountry code="no comma">E-mail: leila.alem@cmis.csiroau</wicri:noCountry></affiliation></author><author><name sortKey="Yacef, Kalina" sort="Yacef, Kalina" uniqKey="Yacef K" first="Kalina" last="Yacef">Kalina Yacef</name><affiliation wicri:level="1"><mods:affiliation>Thomson Radar Australia Corporation and Laboratoire d'Intelligence Artificielle de Paris 5, France</mods:affiliation><country xml:lang="fr">France</country><wicri:regionArea>Thomson Radar Australia Corporation and Laboratoire d'Intelligence Artificielle de Paris 5</wicri:regionArea></affiliation></author></titleStmt><publicationStmt><idno 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xml:lang="fr">Australie</country><wicri:regionArea>CSIRO Mathematical and Information Sciences, Locked bag 17, 2113, North Ryde, NSW</wicri:regionArea></affiliation><affiliation wicri:level="1"><mods:affiliation>E-mail: dongzhang@cmis.csiro.au</mods:affiliation><country wicri:rule="url">Australie</country></affiliation></author><author><name sortKey="Alem, Leila" sort="Alem, Leila" uniqKey="Alem L" first="Leila" last="Alem">Leila Alem</name><affiliation wicri:level="1"><mods:affiliation>CSIRO Mathematical and Information Sciences, Locked bag 17, 2113, North Ryde, NSW, Australia</mods:affiliation><country xml:lang="fr">Australie</country><wicri:regionArea>CSIRO Mathematical and Information Sciences, Locked bag 17, 2113, North Ryde, NSW</wicri:regionArea></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 wicri:level="1"><mods:affiliation>Thomson Radar Australia Corporation and Laboratoire d'Intelligence Artificielle de Paris 5, France</mods:affiliation><country xml:lang="fr">France</country><wicri:regionArea>Thomson Radar Australia Corporation and Laboratoire d'Intelligence Artificielle de Paris 5</wicri:regionArea></affiliation></author></analytic><monogr></monogr><series><title level="s">Lecture Notes in Computer Science</title><imprint><date>1998</date></imprint><idno type="ISSN">0302-9743</idno><idno type="eISSN">1611-3349</idno><idno type="ISSN">0302-9743</idno></series></biblStruct></sourceDesc><seriesStmt><idno type="ISSN">0302-9743</idno></seriesStmt></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" 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><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></record>Pour manipuler ce document sous Unix (Dilib)
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