MIT Libraries homeMIT Libraries logoDSpace@MIT

MIT
View Item 
  • DSpace@MIT Home
  • Computer Science and Artificial Intelligence Lab (CSAIL)
  • Artificial Intelligence Lab Publications
  • AI Technical Reports (1964 - 2004)
  • View Item
  • DSpace@MIT Home
  • Computer Science and Artificial Intelligence Lab (CSAIL)
  • Artificial Intelligence Lab Publications
  • AI Technical Reports (1964 - 2004)
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

Generalizing on Multiple Grounds: Performance Learning in Model-Based Technology

Author(s)
Resnick, Paul
Thumbnail
DownloadAITR-1052.ps (11.09Mb)
Additional downloads
AITR-1052.pdf (4.353Mb)
Metadata
Show full item record
Abstract
This thesis explores ways to augment a model-based diagnostic program with a learning component, so that it speeds up as it solves problems. Several learning components are proposed, each exploiting a different kind of similarity between diagnostic examples. Through analysis and experiments, we explore the effect each learning component has on the performance of a model-based diagnostic program. We also analyze more abstractly the performance effects of Explanation-Based Generalization, a technology that is used in several of the proposed learning components.
Date issued
1989-02-01
URI
http://hdl.handle.net/1721.1/6836
Other identifiers
AITR-1052
Series/Report no.
AITR-1052
Keywords
learning, explanation-based learning, model-basedstroubleshooting

Collections
  • AI Technical Reports (1964 - 2004)

Browse

All of DSpaceCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsThis CollectionBy Issue DateAuthorsTitlesSubjects

My Account

Login

Statistics

OA StatisticsStatistics by CountryStatistics by Department
MIT Libraries homeMIT Libraries logo

Find us on

Twitter Facebook Instagram YouTube RSS

MIT Libraries navigation

SearchHours & locationsBorrow & requestResearch supportAbout us
PrivacyPermissionsAccessibility
MIT
Massachusetts Institute of Technology
Content created by the MIT Libraries, CC BY-NC unless otherwise noted. Notify us about copyright concerns.