TOWARDS INTUITIONISTIC FUZZY COMPUTATIONAL MODELS OF LEARNING

Abstract

Intuitionistic fuzzy sets are defined in 1983 by Atanassov as a generalization of some existing models like fuzzy sets, interval-valued fuzzy sets and L-fuzzy sets. From applicability point of view, the intuitionistic fuzzy modelling provided new vistas in artificial intelligence, expert systems, neural networks, data bases, and decision making, to mention only the main tracks. The usage of intuitionistic fuzzy models in Learning (levels of learning evaluation [1, 7], e-Learning quality evaluation [4, 6], machine learning algorithms [3, 5, 6], quality evaluation [2], etc.) has begun recently. This paper describes the usage of intuitionistic-fuzzy way of thinking and modelling to investigate the Bloom's educational objectives. The first section establishes the aim of the paper and outlines the most important aspects of intuitionistic-fuzzy modelling: state of the art, intuitionistic-fuzzy sets, intuitionistic fuzzy numbers and operators, and applicability. The second section considers the cognitive domain and presents the usage of intuitionistic-fuzzy approach on Bloom's categories: Knowledge, Comprehension, Applicability, Analysis and Synthesis, and Evaluation. The third section is dedicated to the usage of intuitionistic fuzzy machine learning from machine architectures (in general neuro-intuitionistic-fuzzy structures) to their performance evaluation. Finally, concluding remarks on the power of intuitionistic fuzzy approaches to enable creative ways of thinking in research and development are provided. Keywords: Levels of Learning, Machine Learning, Intuitionistic Fuzzy Models and Strategies, e-Learning Selected references [1] Hosseini R., Kardan A., Intuitionistic Fuzzy-Based Method for Assessing the Learner's Knowledge Level and Personalization of Learning Path, Proceedings of ICVL 2011, pp. 441-447, 2011. [2] Hristova M., Sotirova E., Multifactor method of teaching quality estimation at universities with intuitionistic fuzzy evaluation, Twelfth Int. Conf. on IFSs, Sofia, 17-18 May 2008, NIFS Vol. 14(2), pp. 80-83, 2008. [3] Kazakov A. D., On Intuitionistic fuzzy machine learning, 2005, http://www.dmitry- kazakov.de/fuzzy_ai/on_fuzzy_machine_learning.htm [4] Melo-Pinto, P., T. Kim, K. Atanassov, E. Sotirova, A. Shannon and M. Krawczak, Generalized net model of e-learning evaluation with intuitionistic fuzzy estimations, Issues in the Representation and Processing of Uncertain and Imprecise Information, Warszawa, pp. 241-249, 2005. [5] Sotirov S., Atanassov K., Intuitionistic Fuzzy Feed Forward Neural Network, Cybernetics and Information Technologies, 9(2), pp. 62-68, 2009. [6] Sotirov, S., D. Orozova, E. Sotirova, Neural network for defining intuitionistic fuzzy sets in e-learning, Proc. of 13th Int. Conf. on Intuitionistic Fuzzy Sets, Sofia, NIFS Vol. 15(2), pp. 33-36, 2009. [7] Sotirova E., Classification of the students' intuitionistic fuzzy estimations by a 3-dimensional self organizing map, 7th IWIFS, 2011, Conference proceedings, "Notes on IFS", 17(4), pp. 39-44, 2011.