A Theory of Meta-Diagnosis: Reasoning about Diagnostic Systems
Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence. Pages 731-736.
Abstract
In Model-Based Diagnosis, a diagnostic algorithm is typically used to compute diagnoses using a model of a real-world system and some observations. Contrary to classical hypothesis, in real-world applications it is sometimes the case that either the model, the observations or the diagnostic algorithm are abnormal with respect to some required properties; with possibly huge economical consequences. Determining which abnormalities exist constitutes a meta-diagnostic problem. We contribute, first, with a general theory of meta-diagnosis with clear semantics to handle this problem. Second, we propose a series of typically required properties and relate them between themselves. Finally, using our meta-diagnostic framework and the studied properties and relations, we model and solve some common meta-diagnostic problems.
@inproceedings{ijcai2011p129,
title = {A Theory of Meta-Diagnosis: Reasoning about Diagnostic Systems},
author = {Belard, Nuno and Pencolé, Yannick and Combacau, Michel},
booktitle = {Proceedings of the Twenty-Second International Joint Conference on
Artificial Intelligence, {IJCAI-11}},
publisher = {AAAI Press/International Joint Conferences on Artificial Intelligence},
editor = {Toby Walsh},
pages = {731--736},
year = {2011},
month = {7},
doi = {10.5591/978-1-57735-516-8/IJCAI11-129},
url = {https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-129},
}
TY - CPAPER TI - A Theory of Meta-Diagnosis: Reasoning about Diagnostic Systems AU - Belard, Nuno AU - Pencolé, Yannick AU - Combacau, Michel T2 - Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, IJCAI-11 PB - AAAI Press/International Joint Conferences on Artificial Intelligence PY - 2011 SP - 731 EP - 736 DO - 10.5591/978-1-57735-516-8/IJCAI11-129 UR - https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-129 ER -Copied
Machine-readable versions of this paper, for tools and AI agents: