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Construction of normative decision models using abstract graph grammars
Abstract This dissertation addresses automated assi This dissertation addresses automated assistance fordecision analysis in medicine. In particular, I have investigated graphgrammars as a representation for encoding how decision-theoretic models can beconstructed from an unordered list of concerns. The modeling system that Ihave used requires a standard vocabulary to generate decision models; themodels generated are qualitative, and require subsequent assessment ofprobabilities and utility values. This research has focused on the modeling ofthe qualitative structure of problems given a standard vocabulary and giventhat subsequent assessment of probabilities and utilities is possible. Theusefulness of the graph-grammar representation depends on the graph-grammarformalism's ability to describe a broad spectrum of qualitative decisionmodels, on its ability to maintain a high quality in the models it generates,and on its clarity in describing topological constraints to researchers whodesign and maintain the actual grammar. I have found that graph grammars canbe used to generate automatically decision models that are comparable to thoseproduced by decision analysts. ble to thoseproduced by decision analysts.
Address Stanford, CA, USA +
Author John W. Egar +
Bibtype techreport  +
Institution Stanford University +
Key KSL-94-17  +
Modification dateThis property is a special property in this wiki. 1 May 2009 14:06:07  +
Number KSL-94-17  +
Tag Computer science +
Title Construction of Normative Decision Models Using Abstract Graph Grammars  +
Tr id KSL-94-17  +
Year 1994  +
Categories Technical Report, Publication, KSL Technical Report
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