Abstract
It is important for an operator support system to generate an understandable explanation of its complex reasoning process in order to mutually interact with human operators. As a technique to explain the causality inference results based on detailed Multi-level Flow Modelling (MFM) models, a technique to generate essential explanation is studied by applying the technique called function flow simplification to simplify a detailed MFM model by a macroscopic view. Through several explanation generation results for effect estimation of counter actions, the technique is confirmed to generate almost suitable explanation sentences although several differences are found compared with the explanation given by plant experts.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the SICE Annual Conference |
| Pages | 538-541 |
| Number of pages | 4 |
| Publication status | Published - 2005 |
| Event | SICE Annual Conference 2005 - Okayama, Japan Duration: Aug 8 2005 → Aug 10 2005 |
Other
| Other | SICE Annual Conference 2005 |
|---|---|
| Country | Japan |
| City | Okayama |
| Period | 8/8/05 → 8/10/05 |
Keywords
- Causality explanation
- Function flow simplification
- Multi-level flow modelling
- Operator support system
ASJC Scopus subject areas
- Engineering(all)
Cite this
Causality explanation generation based on multi-level flow model for operator support systems. / Gofuku, Akio; Zheng, Yaoyang.
Proceedings of the SICE Annual Conference. 2005. p. 538-541.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Causality explanation generation based on multi-level flow model for operator support systems
AU - Gofuku, Akio
AU - Zheng, Yaoyang
PY - 2005
Y1 - 2005
N2 - It is important for an operator support system to generate an understandable explanation of its complex reasoning process in order to mutually interact with human operators. As a technique to explain the causality inference results based on detailed Multi-level Flow Modelling (MFM) models, a technique to generate essential explanation is studied by applying the technique called function flow simplification to simplify a detailed MFM model by a macroscopic view. Through several explanation generation results for effect estimation of counter actions, the technique is confirmed to generate almost suitable explanation sentences although several differences are found compared with the explanation given by plant experts.
AB - It is important for an operator support system to generate an understandable explanation of its complex reasoning process in order to mutually interact with human operators. As a technique to explain the causality inference results based on detailed Multi-level Flow Modelling (MFM) models, a technique to generate essential explanation is studied by applying the technique called function flow simplification to simplify a detailed MFM model by a macroscopic view. Through several explanation generation results for effect estimation of counter actions, the technique is confirmed to generate almost suitable explanation sentences although several differences are found compared with the explanation given by plant experts.
KW - Causality explanation
KW - Function flow simplification
KW - Multi-level flow modelling
KW - Operator support system
UR - http://www.scopus.com/inward/record.url?scp=33645310193&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=33645310193&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:33645310193
SP - 538
EP - 541
BT - Proceedings of the SICE Annual Conference
ER -