A CONNECTIONIST INCREMENTAL EXPERT SYSTEM COMBINING PRODUCTION SYSTEMS AND ASSOCIATIVE MEMORY
Hong Yin, Ping Liang · International Journal of Pattern Recognition and Artificial Intelligence · 1991
A connectionist expert system model is proposed in this paper. The system combines the capability of production systems and associative memory. Rules are explicitly represented within a network structure. The Perceptron algorithm is generalized to include samples with uncertain components. A gradually-augmented-node learning algorithm is used to guarantee fast memorization of all rules. The convergence of the learning algorithms is analyzed. The system has a dynamic knowledge organization and adapts in real time to acquire new knowledge, or to relearn existing knowledge, through interaction with the user. This allows the system to be built incrementally. An example system is presented.