Title page for ETD etd-0429104-112724

Document Type	thesis
Author Name	Jarvis, Matthew P
URN	etd-0429104-112724
Title	Applying Machine Learning Techniques to Rule Generation in Intelligent Tutoring Systems
Degree	MS
Department	Computer Science
Advisors	Dr. Neil T. Heffernan, Advisor Dr. Stanley M. Selkow, Reader Dr. Michael A. Gennert, Department Head
Keywords	Intelligent Tutoring Systems Model Tracing Machine Learning Artificial Intelligence Programming by Demonstration
Date of Presentation/Defense	2004-04-08
Availability	unrestricted
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.
Files	jarvis.pdf

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