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 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 algorithm 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.
Worcester Polytechnic Institute
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