Representation Requirements for Supporting Decision Model Formulation
This work addresses the challenge of designing better decision-modeling systems for knowledge-based domains, but it appears incremental as it builds on existing representations.
The paper tackled the problem of analyzing representational support for knowledge-based decision-modeling by identifying inference patterns and knowledge types, and gained insights into integrating categorical and uncertain knowledge context-sensitively.
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.