Probabilistic modeling of alarm observation delay in network diagnosis

Kazuo Hashimoto, Kazunori Matsumoto, Norio Shiratori

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

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.

Original languageEnglish
Title of host publicationPRICAI 2000, Topics in Artificial Intelligence - 6th Pacific Rim International Conference on Artificial Intelligence, Proceedings
Pages734-744
Number of pages11
Publication statusPublished - 2000 Dec 1
Externally publishedYes
Event6th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2000 - Melbourne, VIC, Australia
Duration: 2000 Aug 282000 Sep 1

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume1886 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

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

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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