A connectionist shell for developing expert decision support systems

Presents the architecture of a hybrid neural network expert system shell. The system, structured around the concept of network elements, is aimed at preserving the semantic structure of the expert system rules while incorporating the 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 the inferencing process. The system is also able to adjust its inference strategy according to different users and situations. An editor is also provided to enable easy maintenance of the neural network rule elements. The shell is housed in a user-friendly rule-based interface. Two applications that are built upon the abovementioned shell are discussed, they demonstrate the strengths of the network element architecture over conventional rule-based systems.