The ability to map the state of an object into a category languages is transforming AI theories into symbolic strucin a classification hierarchy has long been an important tures. This pattern can be seen in knowledge representapart of many fields, for example, biology and medicine. Gordon and Shortliffe, 1985), and has been especially concerned with applying classification to diagnostic problems. One of the problems in classification is that the relationship between observable evidence and categories is often ambiguous. A piece of evidence can be associated with several categories or can occur with a category in an irregular fashion.

As the technology for building knowledge based systems has matured, important lessons have been learned about the relationship between the architecture of a system and the nature of the problems it is intended to solve. We are implementing a knowledge engineering tool called BART that is designed with these lessons in mind. BART is a Bayesian reasoning tool that makes belief networks and other probabilistic techniques available to knowledge engineers building classificatory problem solvers. BART has already been used to develop a decision aid for classifying ship images, and it is currently being used to manage uncertainty in systems concerned with analyzing intelligence reports. This paper discusses how state-of-the-art probabilistic methods fit naturally into a knowledge based approach to classificatory problem solving, and describes the current capabilities of BART.

In this article, we present a programming language for expressing classificatory problem solvers. CSRL (Conceptual Structures Representation Language) provides structures for representing classification trees, for navigating within those trees, and for encoding uncertainly judgments about the presence of hypotheses. We discuss the motivations, theory, and assumptions that underlie CRSL. Also, some expert systems constructed with CSRL are briefly described.