Representation Requirements for Supporting Decision Model Formulation

2013/03/20 by Tze-Yun Leong, Leong, Tze-Yun
Computer Science · #AI-based Problem Solving and Planning #Artificial Intelligence (cs.AI) #Bayesian Modeling and Causal Inference #FOS: Computer and information sciences #Semantic Web and Ontologies

paper · pdf · doi:10.48550/arxiv.1303.5730

openalex publication_date 2013/03/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Citations

• Extending Term Subsumption systems for Uncertainty Management

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