Exploiting Independence in a Decentralised and Incremental Approach of Diagnosis
Abstract
It is well-known that the size of the model is a bottleneck when using model-based approaches to diagnose complex systems. To answer this problem, decentralised/distributed approaches have been proposed. Another problem, which is far less considered, is the size of the diagnosis itself. However, it can be huge enough, especially in the case of on-line monitoring and when dealing with uncertain observations. We define two independence properties (state and transition-independence) and show their relevance to get a tractable representation of diagnosis in the context of both decentralised and incremental approaches. To illustrate the impact of these properties on the diagnosis size, experimental results on a toy example are given.
Cite
Text
Cordier and Grastien. "Exploiting Independence in a Decentralised and Incremental Approach of Diagnosis." International Joint Conference on Artificial Intelligence, 2007.Markdown
[Cordier and Grastien. "Exploiting Independence in a Decentralised and Incremental Approach of Diagnosis." International Joint Conference on Artificial Intelligence, 2007.](https://mlanthology.org/ijcai/2007/cordier2007ijcai-exploiting/)BibTeX
@inproceedings{cordier2007ijcai-exploiting,
title = {{Exploiting Independence in a Decentralised and Incremental Approach of Diagnosis}},
author = {Cordier, Marie-Odile and Grastien, Alban},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2007},
pages = {292-297},
url = {https://mlanthology.org/ijcai/2007/cordier2007ijcai-exploiting/}
}