Automating Model Acquisition by Fault Knowledge Re-use: Introducing the Diagnostic Remodeler Algorithm

Download
  1. (PDF, 158 KB)
AuthorSearch for:
TypeArticle
ConferenceProceedings of the Canadian Artificial Intelligence Conference, May 21-24,1996., Toronto, Ontario, Canada
AbstractThe paper addresses the problem of automated model acquisition through the re-use of fault knowledge. The Diagnostic Remodeler (DR) algorithm has been implemented for the automated generation of behavioural component models with an explicit representation of function by re-using fault-based knowledge. DR re-uses as its first application the fault knowledge of the Jet Engine Troubleshooting Assistant (JETA). DR extracts a model of the Main Fuel System using real-world engine fault knowledge and two types of background knowledge as input: device dependent and device independent background knowledge. To demonstrate DR's generality, it has also been applied to a coffee maker fault knowledge base to extract the component models of a full coffee device.
Publication date
LanguageEnglish
AffiliationNRC Institute for Information Technology; National Research Council Canada
Peer reviewedNo
NRC number39203
NPARC number5765207
Export citationExport as RIS
Report a correctionReport a correction
Record identifierac8bfbd9-2b51-43c2-8d96-36c83055f97d
Record created2009-03-29
Record modified2016-05-09
Bookmark and share
  • Share this page with Facebook (Opens in a new window)
  • Share this page with Twitter (Opens in a new window)
  • Share this page with Google+ (Opens in a new window)
  • Share this page with Delicious (Opens in a new window)