Exploiting Independence in a Decentralised and Incremental Approach of Diagnosis
Proceedings of the Twentieth International Joint Conference on Artificial Intelligence. Pages 292-296.
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.
@inproceedings{ijcai2007p45,
title = {Exploiting Independence in a Decentralised and Incremental Approach of Diagnosis},
author = {Cordier, Marie-Odile and Grastien, Alban},
booktitle = {Proceedings of the Twentieth International Joint Conference on
Artificial Intelligence, {IJCAI-07}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Ramon López de Mántaras},
pages = {292--296},
year = {2007},
url = {https://ijcai.org/proceedings/2007/45},
}
TY - CPAPER TI - Exploiting Independence in a Decentralised and Incremental Approach of Diagnosis AU - Cordier, Marie-Odile AU - Grastien, Alban T2 - Proceedings of the Twentieth International Joint Conference on Artificial Intelligence, IJCAI-07 PB - International Joint Conferences on Artificial Intelligence PY - 2007 SP - 292 EP - 296 UR - https://ijcai.org/proceedings/2007/45 ER -Copied
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