Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
7-1991
Abstract
This paper outlines a methodology for analyzing the representational support for knowledge-based decision-modeling in a broad domain. A relevant set of inference patterns and knowledge types are identified. By comparing the analysis results to existing representations, some insights are gained into a design approach for integrating categorical and uncertain knowledge in a context sensitive manner.
Discipline
Artificial Intelligence and Robotics | Computer Sciences
Publication
Uncertainty in Artificial Intelligence: Proceedings of the Seventh Conference: July 13-15, 1991, University of California, Los Angeles
First Page
212
Last Page
219
ISBN
9781558602038
Publisher
Morgan Kaufmann
City or Country
San Mateo
Citation
Tze-Yun LEONG.
Representation Requirements for Supporting Decision-Model Formulation. (1991). Uncertainty in Artificial Intelligence: Proceedings of the Seventh Conference: July 13-15, 1991, University of California, Los Angeles. 212-219.
Available at: https://ink.library.smu.edu.sg/sis_research/3063
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
http://worldcat.org/isbn/9781558602038