Conference Proceeding Article
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.
Artificial Intelligence and Robotics | Computer Sciences
Intelligent Systems and Decision Analytics
Uncertainty in Artificial Intelligence: Proceedings of the Seventh Conference: July 13-15, 1991, University of California, Los Angeles
City or Country
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. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/3063
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