Applying Machine Learning Techniques to Rule Generation in Intelligent Tutoring Systems

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, depthfirst 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 suboperations to be generated in a reasonable amount of time, and provides nonprogrammer domain experts with a tool for developing Intelligent Tutoring Systems. © Springer-Verlag 2004 References.