Persistent Link:
http://hdl.handle.net/10150/276534
Title:
AN ADAPTIVE RULE-BASED SYSTEM
Author:
Stackhouse, Christian Paul, 1960-
Issue Date:
1987
Publisher:
The University of Arizona.
Rights:
Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
Abstract:
Adaptive systems are systems whose characteristics evolve over time to improve their performance at a task. A fairly new area of study is that of adaptive rule-based systems. The system studied for this thesis uses meta-knowledge about rules, rulesets, rule performance, and system performance in order to improve its overall performance in a problem domain. An interesting and potentially important phenomenon which emerged is that the performance the system learns while solving a problem appears to be limited by an inherent break-even level of complexity. That is, the cost to the system of acquiring complexity does not exceed its benefit for that problem. If the problem is made more difficult, however, more complexity is required, the benefit of complexity becomes greater than its cost, and the system complexity begins increasing, ultimately to the new break-even point. There is no apparent ultimate limit to the complexity attainable.
Type:
text; Thesis-Reproduction (electronic)
Keywords:
Adaptive control systems.; Expert systems (Computer science); Machine learning.
Degree Name:
M.S.
Degree Level:
masters
Degree Program:
Graduate College; Electrical and Computer Engineering
Degree Grantor:
University of Arizona

Full metadata record

DC FieldValue Language
dc.language.isoen_USen_US
dc.titleAN ADAPTIVE RULE-BASED SYSTEMen_US
dc.creatorStackhouse, Christian Paul, 1960-en_US
dc.contributor.authorStackhouse, Christian Paul, 1960-en_US
dc.date.issued1987en_US
dc.publisherThe University of Arizona.en_US
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en_US
dc.description.abstractAdaptive systems are systems whose characteristics evolve over time to improve their performance at a task. A fairly new area of study is that of adaptive rule-based systems. The system studied for this thesis uses meta-knowledge about rules, rulesets, rule performance, and system performance in order to improve its overall performance in a problem domain. An interesting and potentially important phenomenon which emerged is that the performance the system learns while solving a problem appears to be limited by an inherent break-even level of complexity. That is, the cost to the system of acquiring complexity does not exceed its benefit for that problem. If the problem is made more difficult, however, more complexity is required, the benefit of complexity becomes greater than its cost, and the system complexity begins increasing, ultimately to the new break-even point. There is no apparent ultimate limit to the complexity attainable.en_US
dc.typetexten_US
dc.typeThesis-Reproduction (electronic)en_US
dc.subjectAdaptive control systems.en_US
dc.subjectExpert systems (Computer science)en_US
dc.subjectMachine learning.en_US
thesis.degree.nameM.S.en_US
thesis.degree.levelmastersen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.disciplineElectrical and Computer Engineeringen_US
thesis.degree.grantorUniversity of Arizonaen_US
dc.identifier.proquest1332164en_US
dc.identifier.oclc18621100en_US
dc.identifier.bibrecord.b18396392en_US
All Items in UA Campus Repository are protected by copyright, with all rights reserved, unless otherwise indicated.