Knomes: Knowledge Monitoring Expert System

Abstract

The bottleneck in the process of building the knowledge base of an expert system is the retrieval of the appropriate problem-solving knowledge from the human expert. Methods of knowledge acquisition and representation from the fields of signal processing, pattern recognition and artificial intelligence are considered in this paper. This unified approach will not only accelerate the knowledge acquisition and organization process, but will also formalize and structure the decision making process by reducing the biases of experts. Using this approach, a Knowledge Monitoring Expert System (KNOMES) has been designed to monitor the waveform signals emitting from a material source. The system consists of four primary components, namely; Fact Gathering, Knowledge Base, Knowledge Formalization, and Inference Engine. The fact gathering subsystem 1) collects the transducer(s) emitted signals and extracts a large feature set from them, and 2) collects the a priori real-world knowledge about the source material through an interface monitored by an expert. The facts, a priori real-world knowledge, and the pattern measurements (features) are organized into a knowledge base. The next subsystem formalizes the knowledge into a tree structure using cluster analysis. The tree structure has proven to be an effective method of information organization and statistical pattern recognition. The last subsystem is the Inference Engine whose one of the component primarily classifies the analytical knowledge. This primary classification is done by traversing through the tree and assigning an appropriate class to an unknown input signal. This paper presents the complete design of the proposed system and outlines the implementation details.