Graph-grammar assistance for automated generation of influence diagrams

Authors:
John W. Egar;Mark A. Musen
Affiliations:
Section on Medical Informatics, Stanford University School of Medicine, Stanford, CA;Section on Medical Informatics, Stanford University School of Medicine, Stanford, CA
Venue:
UAI'93 Proceedings of the Ninth international conference on Uncertainty in artificial intelligence
Year:
1993

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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 domains such as medicine, however, exhibit certain prototypical patterns that can guide the modeling process. Medical concepts can be classified according to semantic types that have characteristic positions and typical roles in an influence-diagram model. We have developed a graph-grammar production system that uses such inherent interrelationships among medical terms to facilitate the modeling of medical decisions.