Emulating human decision making process using hybrid system

Abstract

This paper presents the prototype implementation of a hybrid neural network expert system shell. The shell, structured around the concept of `network element', is aimed at preserving semantic structure of the expert system rules whilst incorporating learning capability of neural networks into the inferencing mechanism. Using this architecture, every rule of the knowledge base is represented by a one or two-layer neural network element. These network elements are dynamically linked up to form the rule-tree during inferencing process. Furthermore, the firing of netels emulate opportunistic decision making process, which is typical of human decision makers. Finally, the system is also able to adjust its inference strategy according to different users and situations.