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Understanding Human Learning Using a Multi-agent Simulation of the Unified Learning Model

Understanding Human Learning Using a Multi-agent Simulation of the Unified Learning Model

Vlad Chiriacescu, Leen-Kiat Soh, Duane F. Shell
Copyright: © 2013 |Volume: 7 |Issue: 4 |Pages: 25
ISSN: 1557-3958|EISSN: 1557-3966|EISBN13: 9781466635289|DOI: 10.4018/ijcini.2013100101
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MLA

Chiriacescu, Vlad, et al. "Understanding Human Learning Using a Multi-agent Simulation of the Unified Learning Model." IJCINI vol.7, no.4 2013: pp.1-25. http://doi.org/10.4018/ijcini.2013100101

APA

Chiriacescu, V., Soh, L., & Shell, D. F. (2013). Understanding Human Learning Using a Multi-agent Simulation of the Unified Learning Model. International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), 7(4), 1-25. http://doi.org/10.4018/ijcini.2013100101

Chicago

Chiriacescu, Vlad, Leen-Kiat Soh, and Duane F. Shell. "Understanding Human Learning Using a Multi-agent Simulation of the Unified Learning Model," International Journal of Cognitive Informatics and Natural Intelligence (IJCINI) 7, no.4: 1-25. http://doi.org/10.4018/ijcini.2013100101

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Abstract

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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