Maintaining the utility of learned knowledge using model-based adaptive control
Holder, Lawrence B.
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Permalink
https://hdl.handle.net/2142/22737
Description
Title
Maintaining the utility of learned knowledge using model-based adaptive control
Author(s)
Holder, Lawrence B.
Issue Date
1991
Doctoral Committee Chair(s)
Rendell, Larry A.
Department of Study
Computer Science
Discipline
Computer Science
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
Artificial Intelligence
Computer Science
Language
eng
Abstract
The overfit problem in empirical learning and the utility problem in analytical learning both describe a common behavior of machine learning methods: the eventual degradation of performance due to increasing amounts of learned knowledge. Plotting the performance of the changing knowledge during execution of a machine learning method (the performance response) reveals similar curves for several methods. The performance response generally indicates a single peak performance greater than that attained by popular pruning techniques. The similarity in performance responses suggests a parameterized model relating performance to the amount of learned knowledge. Given this model, a model-based adaptive control (MBAC) approach can be used to update the model based on feedback from the performance element and make control decisions regarding the amount of knowledge to be learned or unlearned.
In view of the large number of alternative learning methods, a more general utility problem exists in determining not only the correct amount of learned knowledge, but also the correct method for learning this knowledge. Relying too heavily on one particular learning method may result in less than optimal performance achievement. Overcoming this general utility problem requires a new control mechanism for determining the correct learning method and amount of learned knowledge in order to achieve the performance objectives of the task. Maintaining models for several learning methods allows the MBAC approach to decide the appropriate type of learning, in addition to the amount.
Experimentation analyzes the ability of the MBAC approach to converge upon the peak of the performance response and avoid generation of low utility knowledge. Results indicate that a quadratic model is sufficient to fit the peak of the performance response and that MBAC using the quadratic model performs well at selecting the best learning method for a given learning task. More formal analysis of the performance response supports the quadratic model for controlling how much knowledge to learn as opposed to which knowledge.
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