  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.
