BIB-VERSION:: CS-TR-v2.0
          ID:: STAN//CS-TR-94-1513
       ENTRY:: May 03, 1994
ORGANIZATION:: Stanford University, Department of Computer Science
       TITLE:: Construction of Normative Decision Models Using Abstract
               Graph Grammars
        TYPE:: Technical Report
        TYPE:: Thesis
      AUTHOR:: Egar, John W.
       PAGES:: 245
    ABSTRACT:: This dissertation addresses automated assistance for decision
               analysis in medicine. In particular, I have investigated
               graph grammars as a representation for encoding how
               decision-theoretic models can be constructed from an
               unordered list of concerns. The modeling system that I have
               used requires a standard vocabulary to generate decision
               models; the models generated are qualitative, and require
               subsequent assessment of probabilities and utility values.
               This research has focused on the modeling of the qualitative
               structure of problems given a standard vocabulary and given
               that subsequent assessment of probabilities and utilities is
               possible. The usefulness of the graph-grammar representation
               depends on the graph-grammar formalism's ability to describe
               a broad spectrum of qualitative decision models, on its
               ability to maintain a high quality in the models it
               generates, and on its clarity in describing topological
               constraints to researchers who design and maintain the actual
               grammar. I have found that graph grammars can be used to
               generate automatically decision models that are comparable to
               those produced by decision analysts.
       NOTES:: Also published as KSL-TR-94-17 by Stanford University,
               Department of Computer Science, Knowledge Systems Laboratory.
               [Adminitrivia V1/Prg/19940503]
         END:: STAN//CS-TR-94-1513