Abstract
The purpose of this research was to apply machine learning techniques to automate rule generation in the construction of Intelligent Tutoring Systems. By using a pair of somewhat intelligent iterative-deepening, depth-first searches, we were able to generate production rules from a set of marked examples and domain background knowledge. Such production rules required independent searches for both the “if” and “then” portion of the rule. This automated rule generation allows generalized rules with a small number of sub-operations to be generated in a reasonable amount of time, and provides non-programmer domain experts with a tool for developing Intelligent Tutoring Systems.
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Jarvis, M.P., Nuzzo-Jones, G., Heffernan, N.T. (2004). Applying Machine Learning Techniques to Rule Generation in Intelligent Tutoring Systems. In: Lester, J.C., Vicari, R.M., Paraguaçu, F. (eds) Intelligent Tutoring Systems. ITS 2004. Lecture Notes in Computer Science, vol 3220. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30139-4_51
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DOI: https://doi.org/10.1007/978-3-540-30139-4_51
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22948-3
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