Graph-Grammar Assistance for Automated Generation of Influence Diagrams

Abstract

One of the most difficult aspects of modeling complex dilemmas in decision-analytic terms is composing a diagram of relevance relations from a set of domain concepts. Decision models in many domains, however, exhibit certain prototypical patterns that can guide the modeling process. Concepts can be classified according to semantic types that have characteristic positions and typical roles in an influence-diagram model. The authors have developed a graph-grammar production system that uses such inherent interrelationships among terms to facilitate the modeling of medical decisions. The authors' system also can examine a set of graph-grammar rules to establish whether the grammar satisfies a number of properties that they have determined to be important in the derivation of influence-diagram models. The authors' findings suggest that syntactic patterns can lead to automated construction of decision models in domains other than medicine. >