TY - GEN
T1 - Understanding human learning using a multi-agent simulation of the Unified Learning Model
AU - Chiriacescu, Vlad
AU - Soh, Leen Kiat
AU - Shell, Duane F.
PY - 2013
Y1 - 2013
N2 - Within cognitive science. 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 and intelligent agents are presented.
AB - Within cognitive science. 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 and intelligent agents are presented.
KW - Cognitive modeling
KW - Computational simulation
KW - Human Learning
KW - Unified Learning Model
UR - http://www.scopus.com/inward/record.url?scp=84889073807&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84889073807&partnerID=8YFLogxK
U2 - 10.1109/ICCI-CC.2013.6622237
DO - 10.1109/ICCI-CC.2013.6622237
M3 - Conference contribution
AN - SCOPUS:84889073807
SN - 9781479907816
T3 - Proceedings of the 12th IEEE International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2013
SP - 143
EP - 152
BT - Proceedings of the 12th IEEE International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2013
T2 - 12th IEEE International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2013
Y2 - 16 July 2013 through 18 July 2013
ER -