Representation Requirements for Supporting Decision Model Formulation

TL;DR

This paper presents a methodology to analyze and improve how decision models are represented, focusing on integrating categorical and uncertain knowledge in a context-sensitive way.

Contribution

It introduces a systematic approach for analyzing representational support for decision-modeling and offers insights into designing integrated knowledge representations.

Findings

Identified key inference patterns and knowledge types for decision-modeling.

Compared existing representations to the analysis, revealing integration challenges.

Provided a design approach for combining categorical and uncertain knowledge.

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.