Uncertainty in Artificial Intelligence
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Knowledge-Based Decision Model Construction for Hierarchical Diagnosis: A Preliminary Report
Soe-Tsyr Yuan
Abstract:
Numerous methods for probabilistic reasoning in large, complex belief or decision networks are currently being developed. There has been little research on automating the dynamic, incremental construction of decision models. A uniform value-driven method of decision model construction is proposed for the hierarchical complete diagnosis. Hierarchical complete diagnostic reasoning is formulated as a stochastic process and modeled using influence diagrams. Given observations, this method creates decision models in order to obtain the best actions sequentially for locating and repairing a fault at minimum cost. This method construct decision models incrementally, interleaving probe actions with model construction and evaluation. The method treats meta-level and baselevel tasks uniformly. That is, the method takes a decision-theoretic look at the control of search in causal pathways and structural hierarchies.
Keywords:
Pages: 274-281
PS Link:
PDF Link: /papers/93/p274-yuan.pdf
BibTex:
@INPROCEEDINGS{Yuan93,
AUTHOR = "Soe-Tsyr Yuan ",
TITLE = "Knowledge-Based Decision Model Construction for Hierarchical Diagnosis: A Preliminary Report",
BOOKTITLE = "Proceedings of the Ninth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-93)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1993",
PAGES = "274--281"
}


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