Abstract
This paper introduces a probabilistic modeling of alarm observation delay, and shows a novel method of model-based diagnosis for time series observation. Firstly, a fault model is denned by associating an event tree rooted by each fault hypothesis with probabilistic variables representing temporal delay. The most probable hypothesis is obtained by selecting one whose AIC (Akaike information criterion) is minimal. It is proved that by simulation that the AIC based hypothesis selection achieves the high precision in diagnosis.
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© 2000 Springer-Verlag Berlin Heidelberg
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Hashimoto, K., Matsumoto, K., Shiratori, N. (2000). Probabilistic Modeling of Alarm Observation Delay in Network Diagnosis. In: Mizoguchi, R., Slaney, J. (eds) PRICAI 2000 Topics in Artificial Intelligence. PRICAI 2000. Lecture Notes in Computer Science(), vol 1886. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44533-1_73
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DOI: https://doi.org/10.1007/3-540-44533-1_73
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-67925-7
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