Modeling expert forecasting knowledge for incorporation into expert systems

Abstract

AbstractThe use of continuous multivariate models to represent experts' knowledge of relations among a set of variables is reviewed. Such knowledge models can be incorporated into expert systems, complementing contingent rules, especially when representing experts' knowledge of functional relations among entities in noisy domains. Past work has most commonly involved linear averaging models in static domains, although nonlinear models and dynamic domains are also possible. Detecting errors in continuous multivariate models requires a different approach from detecting errors in collections of if‐then rules. Methods for eliciting expert knowledge include modeling judgments made in real or hypothetical situations and using expert's self‐insight directly to assist in construction of the model. Procedures for managing each of these methods have been computerized and could be included as elicitation tools in expert system building environments.