| This thesis presents a knowledge acquisition and representation framework for evaluative reasoning tasks, called Candidate Evaluation. It draws from two main research disciplines. First, the decision theoretic model of multi-attribute utility theory (MAUT) provides a mathematical basis for the framework. Second, the knowledge representation is influenced by recent research in task-specific architectures (TSA) and generic tasks (GT). The Candidate Evaluation architecture is implemented in an expert system shell called CEVAL (for Candidate Evaluator), and a development environment called CEVED (for Candidate Evaluation Editor). In addition, an intelligent database combining MAUT with semantic network constructs is presented. The thesis also presents international marketing applications of the Candidate Evaluation model and the MAUT semantic network database. |
