BaRT: A Bayesian Reasoning Tool for Knowledge Based Systems
This work addresses the need for better uncertainty management in knowledge-based systems for domains like image classification and intelligence analysis, representing an incremental improvement by applying existing probabilistic techniques to new applications.
The paper tackles the challenge of integrating probabilistic methods into knowledge-based systems for classificatory problem solving by introducing BaRT, a Bayesian reasoning tool that has been applied to develop a decision aid for ship image classification and is being used for uncertainty management in intelligence report analysis.
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.