One of the most important aspects of current expert systems technology is the ability to make causal inferences about the impact or new evidence. When the domain knowledge and problem knowledge are uncertain and incomplete, Bayesian reasoning has proven to be an effective way or forming such inferences ]3,4,8j. While several reasoning schemes have been developed. based on Ba.yes Rule, there has been very little work examining the comparative effectiveness of these schemes in a real application. This paper describes a knowledge based sys tem for ship classification Ill, originally developed using the PROSPECTOR updating method ]2j, that has been reimplemented to use the inference procedure developed by Pearl and Kim ]4,5]. We discuss our reasons for making this cha.nge, the implementation of the new inference engine, and the comparative performance or the two versions or the system.
As the technology for building knowledge based systems has matured, important lessons have been learned about the relationship between the architec ture 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 reason ing tool that makes belief networks and other prob abilistic techniques available to knowledge engineers building classificatory problem solvers. BART has already been used to develop a decision aid for clas sifying 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 fi t nat urally into a knowledge based approach to classifi catory problem solving, and describes the current capabilities of BART.
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