Understanding Human Learning Using a Multi-agent Simulation of the Unified Learning Model
Within cognitive science and cognitive informatics, computational modeling based on cognitive architectures has been an important approach to addressing questions of human cognition and learning. This paper reports on a multi-agent computational model based on the principles of the Unified Learning...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Chiriacescu, Vlad [verfasserIn] Soh, Leen-Kiat [verfasserIn] Shell, Duane F. [verfasserIn] |
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Englisch |
| Erschienen: |
2013 |
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Enthalten in: International journal of cognitive informatics and natural intelligence - Hershey, Pa : IGI Global, 2007, 7(2013), 4, Seite 1-25 |
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volume:7 ; year:2013 ; number:4 ; pages:1-25 |
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10.4018/ijcini.2013100101 |
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| 520 | |a Within cognitive science and cognitive informatics, computational modeling based on cognitive architectures has been an important approach to addressing questions of human cognition and learning. This paper reports on a multi-agent computational model based on the principles of the Unified Learning Model (ULM). Derived from a synthesis of neuroscience, cognitive science, psychology, and education, the ULM merges a statistical learning mechanism with a general learning architecture. Description of the single agent model and the multi-agent environment which translate the principles of the ULM into an integrated computational model is provided. Validation results from simulations with respect to human learning are presented. Simulation suitability for cognitive learning investigations is discussed. Multi-agent system performance results are presented. Findings support the ULM theory by documenting a viable computational simulation of the core ULM components of long-term memory, motivation, and working memory and the processes taking place among them. Implications for research into human learning, cognitive informatics, intelligent agent, and cognitive computing are presented | ||
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10.4018/ijcini.2013100101 doi (DE-627)NLEJ251793257 (VZGNL)10.4018/ijcini.2013100101 DE-627 ger DE-627 rakwb eng Chiriacescu, Vlad verfasserin aut Understanding Human Learning Using a Multi-agent Simulation of the Unified Learning Model 2013 1 Online-Ressource Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Within cognitive science and cognitive informatics, computational modeling based on cognitive architectures has been an important approach to addressing questions of human cognition and learning. This paper reports on a multi-agent computational model based on the principles of the Unified Learning Model (ULM). Derived from a synthesis of neuroscience, cognitive science, psychology, and education, the ULM merges a statistical learning mechanism with a general learning architecture. Description of the single agent model and the multi-agent environment which translate the principles of the ULM into an integrated computational model is provided. Validation results from simulations with respect to human learning are presented. Simulation suitability for cognitive learning investigations is discussed. Multi-agent system performance results are presented. Findings support the ULM theory by documenting a viable computational simulation of the core ULM components of long-term memory, motivation, and working memory and the processes taking place among them. Implications for research into human learning, cognitive informatics, intelligent agent, and cognitive computing are presented Cognitive Modeling Computational Simulation Human Learning Multi-Agent Unified Learning Model (ULM) Soh, Leen-Kiat verfasserin aut Shell, Duane F. verfasserin aut Enthalten in International journal of cognitive informatics and natural intelligence Hershey, Pa : IGI Global, 2007 7(2013), 4, Seite 1-25 Online-Ressource (DE-627)NLEJ244418780 (DE-600)2381006-3 1557-3966 nnns volume:7 year:2013 number:4 pages:1-25 http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/ijcini.2013100101 X:IGIG Verlag Deutschlandweit zugänglich http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/ijcini.2013100101&buylink=true Abstract ZDB-1-GIS GBV_NL_ARTICLE AR 7 2013 4 1-25 |
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10.4018/ijcini.2013100101 doi (DE-627)NLEJ251793257 (VZGNL)10.4018/ijcini.2013100101 DE-627 ger DE-627 rakwb eng Chiriacescu, Vlad verfasserin aut Understanding Human Learning Using a Multi-agent Simulation of the Unified Learning Model 2013 1 Online-Ressource Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Within cognitive science and cognitive informatics, computational modeling based on cognitive architectures has been an important approach to addressing questions of human cognition and learning. This paper reports on a multi-agent computational model based on the principles of the Unified Learning Model (ULM). Derived from a synthesis of neuroscience, cognitive science, psychology, and education, the ULM merges a statistical learning mechanism with a general learning architecture. Description of the single agent model and the multi-agent environment which translate the principles of the ULM into an integrated computational model is provided. Validation results from simulations with respect to human learning are presented. Simulation suitability for cognitive learning investigations is discussed. Multi-agent system performance results are presented. Findings support the ULM theory by documenting a viable computational simulation of the core ULM components of long-term memory, motivation, and working memory and the processes taking place among them. Implications for research into human learning, cognitive informatics, intelligent agent, and cognitive computing are presented Cognitive Modeling Computational Simulation Human Learning Multi-Agent Unified Learning Model (ULM) Soh, Leen-Kiat verfasserin aut Shell, Duane F. verfasserin aut Enthalten in International journal of cognitive informatics and natural intelligence Hershey, Pa : IGI Global, 2007 7(2013), 4, Seite 1-25 Online-Ressource (DE-627)NLEJ244418780 (DE-600)2381006-3 1557-3966 nnns volume:7 year:2013 number:4 pages:1-25 http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/ijcini.2013100101 X:IGIG Verlag Deutschlandweit zugänglich http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/ijcini.2013100101&buylink=true Abstract ZDB-1-GIS GBV_NL_ARTICLE AR 7 2013 4 1-25 |
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Within cognitive science and cognitive informatics, computational modeling based on cognitive architectures has been an important approach to addressing questions of human cognition and learning. This paper reports on a multi-agent computational model based on the principles of the Unified Learning Model (ULM). Derived from a synthesis of neuroscience, cognitive science, psychology, and education, the ULM merges a statistical learning mechanism with a general learning architecture. Description of the single agent model and the multi-agent environment which translate the principles of the ULM into an integrated computational model is provided. Validation results from simulations with respect to human learning are presented. Simulation suitability for cognitive learning investigations is discussed. Multi-agent system performance results are presented. Findings support the ULM theory by documenting a viable computational simulation of the core ULM components of long-term memory, motivation, and working memory and the processes taking place among them. Implications for research into human learning, cognitive informatics, intelligent agent, and cognitive computing are presented |
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Within cognitive science and cognitive informatics, computational modeling based on cognitive architectures has been an important approach to addressing questions of human cognition and learning. This paper reports on a multi-agent computational model based on the principles of the Unified Learning Model (ULM). Derived from a synthesis of neuroscience, cognitive science, psychology, and education, the ULM merges a statistical learning mechanism with a general learning architecture. Description of the single agent model and the multi-agent environment which translate the principles of the ULM into an integrated computational model is provided. Validation results from simulations with respect to human learning are presented. Simulation suitability for cognitive learning investigations is discussed. Multi-agent system performance results are presented. Findings support the ULM theory by documenting a viable computational simulation of the core ULM components of long-term memory, motivation, and working memory and the processes taking place among them. Implications for research into human learning, cognitive informatics, intelligent agent, and cognitive computing are presented |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">NLEJ251793257</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20231205143842.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">231128s2013 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.4018/ijcini.2013100101</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)NLEJ251793257</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(VZGNL)10.4018/ijcini.2013100101</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Chiriacescu, Vlad</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Understanding Human Learning Using a Multi-agent Simulation of the Unified Learning Model</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2013</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 Online-Ressource</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Within cognitive science and cognitive informatics, computational modeling based on cognitive architectures has been an important approach to addressing questions of human cognition and learning. This paper reports on a multi-agent computational model based on the principles of the Unified Learning Model (ULM). Derived from a synthesis of neuroscience, cognitive science, psychology, and education, the ULM merges a statistical learning mechanism with a general learning architecture. Description of the single agent model and the multi-agent environment which translate the principles of the ULM into an integrated computational model is provided. Validation results from simulations with respect to human learning are presented. Simulation suitability for cognitive learning investigations is discussed. Multi-agent system performance results are presented. Findings support the ULM theory by documenting a viable computational simulation of the core ULM components of long-term memory, motivation, and working memory and the processes taking place among them. Implications for research into human learning, cognitive informatics, intelligent agent, and cognitive computing are presented</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">Cognitive Modeling</subfield><subfield code="a">Computational Simulation</subfield><subfield code="a">Human Learning</subfield><subfield code="a">Multi-Agent</subfield><subfield code="a">Unified Learning Model (ULM)</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Soh, Leen-Kiat</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Shell, Duane F.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">International journal of cognitive informatics and natural intelligence</subfield><subfield code="d">Hershey, Pa : IGI Global, 2007</subfield><subfield code="g">7(2013), 4, Seite 1-25</subfield><subfield code="h">Online-Ressource</subfield><subfield code="w">(DE-627)NLEJ244418780</subfield><subfield code="w">(DE-600)2381006-3</subfield><subfield code="x">1557-3966</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:7</subfield><subfield code="g">year:2013</subfield><subfield code="g">number:4</subfield><subfield code="g">pages:1-25</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/ijcini.2013100101</subfield><subfield code="m">X:IGIG</subfield><subfield code="x">Verlag</subfield><subfield code="z">Deutschlandweit zugänglich</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/ijcini.2013100101&buylink=true</subfield><subfield code="3">Abstract</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-1-GIS</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_NL_ARTICLE</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">7</subfield><subfield code="j">2013</subfield><subfield code="e">4</subfield><subfield code="h">1-25</subfield></datafield></record></collection>
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