LBS: Bayesian learning system for rapid expert system development

摘要

This paper presents the results of experience with a novel expert system shell called Learning Base System (LBS). The primary goal in developing LBS was to enable the rapid development of testable expert systems. The strategy adopted was to use a Bayesian classifier system as the form of knowledge representation, and adapt it to allow incremental acquisition-of knowledge from both data and experts, and prediction and validation procedures. The advantages and limitations of the system are described in three applications. The first application is the diagnosis of diseases in crops, illustrating knowledge acquisition by an expert in a data-poor domain. The second illustrates how LBS could be used in a geographic information system. The third is the development and testing of models for predicting wildlife density solely from data. The Bayesian classifier is shown to be a flexible formalism for implementing a wide variety of knowledge-based tasks.