Hierarchical Diagnosis Guided by Observations
Abstract
We propose a technique to improve the performance of hierarchical model-based diagnosis, based on structural abstraction. Given a hierarchical representation and the set of currently available observations, the technique is able to dynamically derive a tailored hierarchical representation to diagnose the current situation. We implement our strategy as an extension to the well-known Mozetic's approach [Mozetic, 1992], and illustrate the obtained performance improvements. Our approach is more efficient than Mozetic's one when, due to abstraction, fewer observations are available at the coarsest hierarchical levels.
Cite
Text
Chittaro and Ranon. "Hierarchical Diagnosis Guided by Observations." International Joint Conference on Artificial Intelligence, 2001.Markdown
[Chittaro and Ranon. "Hierarchical Diagnosis Guided by Observations." International Joint Conference on Artificial Intelligence, 2001.](https://mlanthology.org/ijcai/2001/chittaro2001ijcai-hierarchical/)BibTeX
@inproceedings{chittaro2001ijcai-hierarchical,
title = {{Hierarchical Diagnosis Guided by Observations}},
author = {Chittaro, Luca and Ranon, Roberto},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2001},
pages = {573-578},
url = {https://mlanthology.org/ijcai/2001/chittaro2001ijcai-hierarchical/}
}