Fusion of knowledge-based systems and neural networks and applications

Abstract

Neural Networks and Symbolic Knowledge-Based Systems each have their strengths and weaknesses. Intelligent Systems that fuse these two paradigms overcome a significant number of the weaknesses of each individual paradigm. There are many different approaches in the literature (including research from the author's own group) to fusing these paradigms. A critical evaluation of these approaches is given within the paper.