Uncertainty in Artificial Intelligence
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Graph-Grammar Assistance for Automated Generation of Influence Diagrams
John Egar, Mark Musen
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:
Pages: 235-242
PS Link:
PDF Link: /papers/93/p235-egar.pdf
BibTex:
@INPROCEEDINGS{Egar93,
AUTHOR = "John Egar and Mark Musen",
TITLE = "Graph-Grammar Assistance for Automated Generation of Influence Diagrams",
BOOKTITLE = "Proceedings of the Ninth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-93)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1993",
PAGES = "235--242"
}


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