An adaptive model-based diagnostic system:

Abstract: "This paper describes an experimental diagnostic system which explores adaptability with learning capability from its experience. It takes a model based reasoning approach, utilizing experiential knowledge at the same time. Experiential knowledge consists of cached symptom-failure ass...

Ausführliche Beschreibung

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Bibliographische Detailangaben
Hauptverfasser: Koseki, Yoshiyuki (VerfasserIn), Nakakuki, Yoichiro (VerfasserIn), Tanaka, Midori (VerfasserIn)
Format: Buch
Sprache:English
Veröffentlicht: Tokyo, Japan 1990
Schriftenreihe:Shin-Sedai-Konpyūta-Gijutsu-Kaihatsu-Kikō <Tōkyō>: ICOT technical memorandum 957
Schlagworte:
Zusammenfassung:Abstract: "This paper describes an experimental diagnostic system which explores adaptability with learning capability from its experience. It takes a model based reasoning approach, utilizing experiential knowledge at the same time. Experiential knowledge consists of cached symptom-failure association rules and a probability model of the target system components. It is able to generate and select appropriate tests according to the failure probability distribution. With this capability, when the system has had a similar experience in the past, it can diagnose the failure faster and more efficiently by suggesting better tests
Therefore, this system gives a solution to both the knowledge acquisition bottleneck problem of rule-based systems and the efficiency problem of model-based systems. It is being implemented in ESP language on a prolog machine PSI-II. The effectiveness of the technique is shown by an experimental result.
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