Knowledge modeling in integrated symbolic-connectionist systems

Knowledge based systems, fuzzy systems, and artificial neural networks (ANN) are the three most widely used computational paradigms for emulating different aspects of human cognition like information processing, knowledge representation, and learning. A proper integration of these three paradigms can help to realize powerful problem solving strategies especially for large data intensive domains. In this direction we have developed a generic architecture for integration of symbolic (knowledge based and fuzzy) and connectionist (ANN) systems for large, data intensive domains at the task structure level, computational (symbol) level, and the program level. In this paper we outline the knowledge modeling aspects of the integrated symbolic (knowledge based and fuzzy)-connectionist architecture. The knowledge content of the architecture can facilitate a problem solver in modeling the knowledge required for using as well as integrating the three intelligent paradigms.