Probabilistic modeling of alarm observation delay in network diagnosis

Kazuo Hashimoto*, Kazunori Matsumoto, Norio Shiratori

*この研究の対応する著者

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

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.

本文言語English
ホスト出版物のタイトルPRICAI 2000, Topics in Artificial Intelligence - 6th Pacific Rim International Conference on Artificial Intelligence, Proceedings
ページ734-744
ページ数11
出版ステータスPublished - 2000 12 1
外部発表はい
イベント6th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2000 - Melbourne, VIC, Australia
継続期間: 2000 8 282000 9 1

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
1886 LNAI
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

Conference

Conference6th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2000
国/地域Australia
CityMelbourne, VIC
Period00/8/2800/9/1

ASJC Scopus subject areas

  • 理論的コンピュータサイエンス
  • コンピュータ サイエンス(全般)

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