The Artificial Intelligence Applications to O-level Maintenance (AIATOM) expert system is the product of research into how artificial intelligence experience-based learning can improve the accuracy and cost effectiveness of fault diagnosis in a military maintenance environment. An adaptive diagnostic maintenance advisor, developed using LISP on a VAX system, learns new symptoms and forms new associations between sets of known symptoms and maintenance actions. However, the system does not learn new maintenance actions. The current system has avionic and nonavionic knowledge bases for the nose wheel steering system and the stores management system, respectively, of the US Navy F/A 18 Hornet. The general capabilities of the user interface in AIATOM and the design of an adaptive-learning maintenance-assistant system are described.


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    Title :

    An adaptive-learning expert system for maintenance diagnostics


    Additional title:

    Ein adaptiv-lernendes Expertensystem fuer die Wartungsdiagnostik


    Contributors:
    Tran, L.P. (author) / Hancock, J.P. (author)


    Publication date :

    1989


    Size :

    6 Seiten, 4 Quellen


    Type of media :

    Conference paper


    Type of material :

    Print


    Language :

    English




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