• DocumentCode
    1187974
  • Title

    Graph-grammar assistance for automated generation of influence diagrams

  • Author

    Egar, John W. ; Musen, Mark A.

  • Author_Institution
    Sch. of Med., Stanford Univ., CA, USA
  • Volume
    24
  • Issue
    11
  • fYear
    1994
  • fDate
    11/1/1994 12:00:00 AM
  • Firstpage
    1625
  • Lastpage
    1642
  • 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
  • Keywords
    decision theory; grammars; knowledge acquisition; knowledge based systems; automated generation; complex dilemmas; decision models; decision-analytic terms; domain concepts; graph-grammar assistance; graph-grammar production system; influence diagrams; medical decisions; semantic types; syntactic patterns; Biomedical informatics; Databases; Decision making; Knowledge acquisition; Medical treatment; Performance analysis; Probability distribution; Production systems; Prototypes; Testing;
  • fLanguage
    English
  • Journal_Title
    Systems, Man and Cybernetics, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0018-9472
  • Type

    jour

  • DOI
    10.1109/21.328912
  • Filename
    328912