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Interactive Learning Environments for Fault Diagnosis

Authors: [tex2html_wrap4438]K. Sandrasegaran, A.S. Malowany

Investigator username: malowany

Category: expert systems

Subcategory:

The major bottleneck in the development of such environments has been the acquisition and application of diagnostic knowledge of a chosen device under various faulty conditions. The traditional approach of interviewing human experts for encoding this knowledge is time consuming, may lack co-operation, and the knowledge acquired cannot be applied to other devices and domains. Our research focusses on the automated generation of fault diagnostic knowledge from a description of the structure of a device. The major contributions consist of an integrated knowledge representation methodology for devices, a language for describing device causality, and a meta-knowledge base methodology for automated generation of fault diagnostic knowledge. Most of the terminology and theories used in our methodology is domain-independent, making it applicable for a wide variety of domains and devices with minor modifications.


baron@cim.mcgill.ca