Diagnosis of Technical Systems
Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence. Doctoral Consortium. Pages 4375-4375.
Abstract
Increasing complexity of technical systems requires a precise fault localization in order to reduce maintenance costs and system downtimes. Model-based diagnosis has been presented as a method to derive root causes for observed symptoms, utilizing a description of the system to be diagnosed. Practical applications of model-based diagnosis, however, are often prevented by the initial modeling task and computational complexity associated with diagnosis. In the proposed thesis, we investigate techniques addressing these issues. In particular, we utilize a mapping function which converts fault information available in practice into propositional horn logic sentences to be used in abductive model-based diagnosis. Further, we plan on devising algorithms which allow an efficient computation of explanations given the obtained models.
@inproceedings{ijcai2015p635,
title = {Diagnosis of Technical Systems},
author = {Koitz, Roxane and Wotawa, Franz},
booktitle = {Proceedings of the Twenty-Fourth International Joint Conference on
Artificial Intelligence, {IJCAI-15}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Qiang Yang and Michael Wooldridge},
pages = {4375--4375},
year = {2015},
month = {7},
note = {Doctoral Consortium},
url = {https://ijcai.org/proceedings/2015/635},
}
TY - CPAPER TI - Diagnosis of Technical Systems AU - Koitz, Roxane AU - Wotawa, Franz T2 - Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-15 PB - International Joint Conferences on Artificial Intelligence PY - 2015 SP - 4375 EP - 4375 UR - https://ijcai.org/proceedings/2015/635 ER -Copied
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