Graph-Grammar Assistance for Automated Generation of Influence Diagrams

@article{Egar1993GraphGrammarAF,
  title={Graph-Grammar Assistance for Automated Generation of Influence Diagrams},
  author={J. W. Egar and M. Musen},
  journal={IEEE Trans. Syst. Man Cybern. Syst.},
  year={1993},
  volume={24},
  pages={1625-1642}
}
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… Expand
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References

SHOWING 1-10 OF 60 REFERENCES
Automated modeling of medical decisions.
  • J. W. Egar, M. Musen
  • Computer Science, Medicine
  • Proceedings. Symposium on Computer Applications in Medical Care
  • 1993
TLDR
A graph grammar and a graph-grammar derivation system that generate decision-theoretic models from unordered lists of medical terms, and ensures that several desirable structural properties are maintained in all derived decision models. Expand
An Introduction to Graph-Based Modeling Systems, Part II: Graph-Grammars and the Implementation
TLDR
This concluding part presents an overview of the concept of a Graph-Based Modeling System (GBMS), which supports the construction of a variety of models as long as the models can be expressed as attributed graphs. Expand
Reframing Decision Problems: A Graph-Grammar Approach
TLDR
A modeling environment in which decision-trees are cast as attributed-graphs, and reframing operations on trees are implemented as graph-grammar productions is presented, to illustrate how a general-purpose modeling environment can be used to produce a specialized decision support system for problems that have a strong graphical orientation. Expand
Structuring Conditional Relationships in Influence Diagrams
TLDR
This paper extends the definition of an influence diagram by introducing a new representation for its conditional probability distributions, which allows one to clearly and efficiently represent asymmetric decision problems and provides an attractive alternative to both the decision tree and conventional influence diagram representations. Expand
Automated Critiquing of Medical Decision Trees
TLDR
A decision tree-critiquing program (called BUNYAN) that identifies potential modeling errors in medical decision trees and constitutes the core of a methodology for reasoning about decision models. Expand
Graph Grammars and Their Application to Computer Science
Graph Grammars and Logic Programming
TLDR
The main result of the paper states the soundness and completeness of the representation of clauses by productions, and this correspondence is extended to entire computations, showing how a context-free grammar (over a suitable category of graphs) can be associated with a logic program. Expand
Graph-Grammars and Their Application to Computer Science
TLDR
An efficient algorithm for the solution of hierarchical networks of constraints and a software development environment based on graph technology are introduced. Expand
Representation of clinical data using SNOMED III and conceptual graphs.
  • K. E. Campbell, M. Musen
  • Computer Science, Medicine
  • Proceedings. Symposium on Computer Applications in Medical Care
  • 1992
TLDR
Application of conceptual-graph formalisms to SNOMED III can ensure consistency in its use across different institutions, and allow mapping of the resulting SNOMed III codes onto relational data models and onto other formal systems, such as first-order predicate calculus. Expand
Optimizing the Structure of a Standardized Vocabulary-The SNOMED Model.
TLDR
The newest version of SNOMED has each of the minimal structural properties required for a vocabulary and is offered as a candidate for concept representation in medicine that is needed to support a knowledge base. Expand
...
1
2
3
4
5
...