Probabilistic Reasoning About Ship Images

TL;DR

This paper compares Bayesian inference schemes in a ship classification expert system, highlighting the transition from PROSPECTOR to Pearl and Kim's method and analyzing their relative effectiveness.

Contribution

It introduces a reimplementation of a ship classification system using Pearl and Kim's inference procedure and compares its performance with the original PROSPECTOR-based system.

Findings

Pearl and Kim's method improved inference accuracy

The new inference engine showed better handling of uncertain evidence

Comparison revealed strengths and weaknesses of both schemes

Abstract

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.