Probabilistic Reasoning About Ship Images
This work addresses the need for evaluating Bayesian inference methods in a real-world application for ship classification experts, but it is incremental as it focuses on comparing existing schemes rather than introducing new ones.
The paper tackles the problem of comparing Bayesian reasoning schemes for ship classification by reimplementing a knowledge-based system originally using the PROSPECTOR method with Pearl and Kim's inference procedure, and it reports on the comparative performance of the two versions.
One of the most important aspects of current expert systems technology is the ability to make causal inferences about the impact of new evidence. When the domain knowledge and problem knowledge are uncertain and incomplete Bayesian reasoning has proven to be an effective way of forming such inferences [3,4,8]. While several reasoning schemes have been developed based on Bayes Rule, there has been very little work examining the comparative effectiveness of these schemes in a real application. This paper describes a knowledge based system for ship classification [1], originally developed using the PROSPECTOR updating method [2], that has been reimplemented to use the inference procedure developed by Pearl and Kim [4,5]. We discuss our reasons for making this change, the implementation of the new inference engine, and the comparative performance of the two versions of the system.