Uncertainty in Artificial Intelligence
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Supply Restoration in Power Distribution Systems - A Case Study in Integrating Model-Based Diagnosis and Repair Planning
Sylvie Thiebaux, Marie-Odile Cordier, Olivier Jehl, Jean-Paul Krivine
Abstract:
Integrating diagnosis and repair is particularly crucial when gaining sufficient information to discriminate between several candidate diagnoses requires carrying out some repair actions. A typical case is supply restoration in a faulty power distribution system. This problem, which is a major concern for electricity distributors, features partial observability, and stochastic repair actions which are more elaborate than simple replacement of components. This paper analyses the difficulties in applying existing work on integrating model-based diagnosis and repair and on planning in partially observable stochastic domains to this real-world problem, and describes the pragmatic approach we have retained so far.
Keywords: Experiments with methods for diagnosis and planning,model-based diagnosis and repair,
Pages: 525-532
PS Link: http://csl.anu.edu.au/~thiebaux/papers/uai96.ps.gz
PDF Link: /papers/96/p525-thiebaux.pdf
BibTex:
@INPROCEEDINGS{Thiebaux96,
AUTHOR = "Sylvie Thiebaux and Marie-Odile Cordier and Olivier Jehl and Jean-Paul Krivine",
TITLE = "Supply Restoration in Power Distribution Systems - A Case Study in Integrating Model-Based Diagnosis and Repair Planning",
BOOKTITLE = "Proceedings of the Twelfth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-96)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1996",
PAGES = "525--532"
}


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