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 language | English |
|---|---|
| Title of host publication | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
| Pages | 734-744 |
| Number of pages | 11 |
| Volume | 1886 LNAI |
| Publication status | Published - 2000 |
| Externally published | Yes |
| Event | 6th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2000 - Melbourne, VIC Duration: 2000 Aug 28 → 2000 Sep 1 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 1886 LNAI |
| ISSN (Print) | 03029743 |
| ISSN (Electronic) | 16113349 |
Other
| Other | 6th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2000 |
|---|---|
| City | Melbourne, VIC |
| Period | 00/8/28 → 00/9/1 |
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ASJC Scopus subject areas
- Computer Science(all)
- Theoretical Computer Science
Cite this
Probabilistic modeling of alarm observation delay in network diagnosis. / Hashimoto, Kazuo; Matsumoto, Kazunori; Shiratori, Norio.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 1886 LNAI 2000. p. 734-744 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 1886 LNAI).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Probabilistic modeling of alarm observation delay in network diagnosis
AU - Hashimoto, Kazuo
AU - Matsumoto, Kazunori
AU - Shiratori, Norio
PY - 2000
Y1 - 2000
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84867810439&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84867810439&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84867810439
SN - 3540679251
SN - 9783540679257
VL - 1886 LNAI
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 734
EP - 744
BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ER -