By Topic

Graph-grammar assistance for automated generation of influence diagrams

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
J. W. Egar ; Sch. of Med., Stanford Univ., CA, USA ; M. A. Musen

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

Published in:

IEEE Transactions on Systems, Man, and Cybernetics  (Volume:24 ,  Issue: 11 )