Abstract
This study presents a compilation of techniques for Knowledge Representation (KR) in Intelligent Tutoring System (ITS). Shows pros and cons of each approach in order to use the proper technique according to the needs. Analyses literature related to ITS and KR to find the approaches. Highlights: Fuzzy Cognitive Maps, Bayesian Network, Semantic Networks, Graphs, among other methods. Each approach contributes with elements to model knowledge. We made a comparison of each model with determined factors. Each technique of KR provides his own vision of how the world should look. Besides, it shows what information is necessary to represent and what is important to ignore. Different approaches to intelligent reasoning lead to different goals and definitions of success.
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Ramírez-Noriega, A., Juárez-Ramírez, R., Jiménez, S., Martínez-Ramírez, Y. (2017). Knowledge Representation in Intelligent Tutoring System. In: Hassanien, A., Shaalan, K., Gaber, T., Azar, A., Tolba, M. (eds) Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2016. AISI 2016. Advances in Intelligent Systems and Computing, vol 533. Springer, Cham. https://doi.org/10.1007/978-3-319-48308-5_2
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