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March 2013
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.
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Decision making is often complicated by the dy-namism and uncertainty of the information involved. This paper presents a new methodology for formulating, analyzing, and solving dynamic decision problems. The proposed formalism inte-grates multi-disciplinary approaches; it also generalizes current dynamic decision modeling frameworks. The ontology, graphical presenta-tion, and mathematical representation of the decision factors are distinguished; modular exten-sions to the scope of the admissible decision problems and systematic improvements to the ef-ficiency of their solutions are supported.
