Report Number: CS-TR-94-1513
Institution: Stanford University, Department of Computer Science
Title: Construction of Normative Decision Models Using Abstract Graph Grammars
Author: Egar, John W.
Date: May 1994
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.