Representation requirements for supporting decision model formulation

  • Authors:
  • Tze-Yun Leong

  • Affiliations:
  • MIT Laboratory for Computer Science, Cambridge, MA

  • Venue:
  • UAI'91 Proceedings of the Seventh conference on Uncertainty in Artificial Intelligence
  • Year:
  • 1991

Quantified Score

Hi-index 0.00

Visualization

Abstract

This paper outlines a methodology for analyzing the representational support for knowledgebased 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.