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A distributed blackboard-based architecture for tele-diagnosis

Published online by Cambridge University Press:  27 February 2009

Jessica Ronchi ,
Grazia Butera ,
Enrico Frascari  and
Piero Scaruffi
Jessica Ronchi
Affiliation:
Olivetti, Artificial Intelligence Center, 20300 Stevens Creek Boulevard, Cupertino, CA 94015, U.S.A.
Grazia Butera
Affiliation:
Olivetti, Artificial Intelligence Center, 20300 Stevens Creek Boulevard, Cupertino, CA 94015, U.S.A.
Enrico Frascari
Affiliation:
Olivetti, Artificial Intelligence Center, 20300 Stevens Creek Boulevard, Cupertino, CA 94015, U.S.A.
Piero Scaruffi
Affiliation:
Olivetti, Artificial Intelligence Center, 20300 Stevens Creek Boulevard, Cupertino, CA 94015, U.S.A.
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Abstract

KANT is a knowledge-based system designed to diagnose Olivetti personal computers connected as remote terminals to a host computer through a SNA link. KANT is a collection of a few knowledge-based units: some of them operate in the field, and some operate back at the home office. They are configured in two blackboard systems which exchange data via the SNA link. The first blackboard runs on a cheap personal computer, only employs shallow knowledge, and performs the diagnoses that can be achieved in the field. The second blackboard runs on corporate mainframes, employs deep knowledge, and supports the more sophisticated analysis that is required from the project team for fixing complex problems.

Type
Research Article
Information
AI EDAM , Volume 1 , Issue 2 , May 1987 , pp. 103 - 108
Copyright
Copyright © Cambridge University Press 1987

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References

Davis, R., 1984. Diagnostic reasoning based on structure and behavior. Artificial Intelligence 24, 347–410.CrossRefGoogle Scholar
Hayes-Roth, B 1984. BB1: An architecture for blackboard systems. Report STAN-CS-84–1034, Stanford University, Stanford, CA, pp. 1–120.Google Scholar
Hayes-Roth, B., 1985. Blackboard architecture for control. Artificial Intelligence 26(3), 251–321.CrossRefGoogle Scholar
Nij, H. P., 1986. Blackboard systems. Report KSL-86–18, 1986, Stanford University, Stanford, CA, pp. 1–86.Google Scholar
Reiter, R., 1987. A theory of diagnosis from first principles. Artificial Intelligence 32(1), 57–95.CrossRefGoogle Scholar
DeKleer, J., 1987. Diagnosing multiple faults. Artificial Intelligence 32(1), 97–130.CrossRefGoogle Scholar