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.

Creator

• Jarvis, Matthew P

Contributors

• Heffernan, Neil

Degree

• MS

Unit

• Computer Science

Publisher

• Worcester Polytechnic Institute

Language

• English

Identifier

• etd-0429104-112724

Keyword

• Programming by Demonstration
• Intelligent tutoring systems
• Model Tracing
• Machine Learning
• Artificial Intelligence

Advisor

• Heffernan, Neil

Defense date

• 2004-04-08

Year

• 2004

Date created

• 2004-04-29

Resource type

• Thesis

Rights statement

• In Copyright

Last modified

• 2021-02-01