Abstract
Knowledge base is an important component in intelligent problems solver. Based on knowledge base, the inference engine of this system can be designed to solve problems in the knowledge domain. Ontology emerges as a potent methodology for the formulation of the knowledge base in intelligent systems. In this paper, a method for integrating of ontology and functional knowledge is proposed. This ontology, which represents relational knowledge, plays a foundation to connect with other intellectual components. The integrating model, called Rela-Funcs model, is useful to represent knowledge domains of functions. The study delves deeper into the functional intellectual component, exploring robust knowledge representation methods and automated inference algorithms tailored to this crucial element. Based on this model, an intelligent problems solver in high-school 2D-Analytic geometry is proposed. This application delivers clear, step-by-step explanations, facilitating student learning and research through its pedagogical design and alignment with student reasoning patterns.
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This research was supported by The VNUHCM-University of Information Technology's Scientific Research Support Fund.
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Tran, N.P., Nguyen, H.D., Nguyen, D., Tran, D.A., Huynh, A.T., Le, T.T. (2024). A Method for Integrating of Knowledge Model and Functional Component and Application in Intelligent Problem Solver. In: Fujita, H., Cimler, R., Hernandez-Matamoros, A., Ali, M. (eds) Advances and Trends in Artificial Intelligence. Theory and Applications. IEA/AIE 2024. Lecture Notes in Computer Science(), vol 14748. Springer, Singapore. https://doi.org/10.1007/978-981-97-4677-4_13
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