CEG: A case based decision modeling architecture

In this paper we present a case evidence generation architecture, CEG, that supports case based decision and knowledge modeling. CEG is a blackboard based system that supports case evidence generation and acquisition. It can be used to define case decision classes, to model case knowledge elements, to represent prototypical case features and cases, to index cases according to decision classes or prototypical case realizations and to model elements of bias or error in case representation. We place CEG in the context of multicriteria decision making and draw architectural components from Machine Learning. We give an overview of CEG and CEG knowledge components. We demonstrate CEG by means of two ‘real world’ decision making exemplars drawn from retail sales evaluation and medical decision making. Finally, we explore architectural properties and discuss the potential for further research on the subject.