Selective evidence gathering for diagnostic belief networks

Abstract

The belief network framework for reasoning with uncertainty in knowledgebased systems has been around for some time now. As more and more practical applications employing the framework are being developed, it becomes apparent that the framework lacks with regard to explicit means for exerting control over reasoning. In this paper, we extend the belief network framework with a method for selective gathering of evidence for diagnostic applications. To this end, a belief network architecture is developed consisting of two layers: a probabilistic layer specifying a belief network and its associated algorithms, and a control layer providing the method for evidence gathering.