  Automated decision making is often complicated by the complexity of the
knowledge involved. Much of this complexity arises from the context sensitive
variations of the underlying phenomena. We propose a framework for representing
descriptive, context-sensitive knowledge. Our approach attempts to integrate
categorical and uncertain knowledge in a network formalism. This paper outlines
the basic representation constructs, examines their expressiveness and
efficiency, and discusses the potential applications of the framework.
