Abstract
The Towers of Hanoi is a mathematical problem, which consists of three pegs, and a number “n” of disks of distinct sizes which can slide onto any peg. A cognitive model is a theoretical, empirical and computational representation of mental processes which belong to a cognitive function. A cognitive model generates a human-like performance for developing tasks, correcting errors, using strategies, and acquiring knowledge. A cognitive model constructed in a cognitive architecture is characterized to be runnable and producing specific behaviors. A cognitive architecture is a general-purpose control framework based on scientific theories to specify computational models of human cognitive performance. CARINA is a metacognitive architecture for artificial intelligent agents, obtained from the MISM Metacognitive Meta model. The objective of this paper is to present a cognitive model based on NGOMS-L to solve the algorithm of the Towers of Hanoi that can be runnable in the metacognitive architecture CARINA. The methodology used for the analysis of the cognitive task was: pre-processing stage, processing stage, classification of subjects, description of cognitive task in natural language and finally description of the cognitive task in NGOMS-L. The results obtained showed that of the four subjects originally selected, three of them were able to solve the problem and only one abandoned the problem. In the classification made, a successful and an unsuccessful subject was selected, to represent the cognitive task in natural language and finally express in the NGOMS-L notation the different Goals, Operators, Methods, and Selection Rules.
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Flórez, Y.P., Jerónimo, A.J., Castillo, M.E., Gómez, A.A. (2020). User-Based Cognitive Model in NGOMS-L for the Towers of Hanoi Algorithm in the Metacognitive Architecture CARINA. In: Botto-Tobar, M., León-Acurio, J., Díaz Cadena, A., Montiel Díaz, P. (eds) Advances in Emerging Trends and Technologies. ICAETT 2019. Advances in Intelligent Systems and Computing, vol 1066. Springer, Cham. https://doi.org/10.1007/978-3-030-32022-5_44
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