Graph-Grammar Assistance for Automated Generation of Influence Diagrams
Artificial Intelligence
2013-03-08 v1
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.
Keywords
Cite
@article{arxiv.1303.1482,
title = {Graph-Grammar Assistance for Automated Generation of Influence Diagrams},
author = {John W. Egar and Mark A. Musen},
journal= {arXiv preprint arXiv:1303.1482},
year = {2013}
}
Comments
Appears in Proceedings of the Ninth Conference on Uncertainty in Artificial Intelligence (UAI1993)