Integration of neural networks with knowledge-based systems

Existing prejudices of some artificial intelligence researchers against neural networks are hard to break. One of their most important arguments is that neural networks are not able to explain their decisions. They also claim that neural networks are not able so solve the variable binding problem for unification. We show in this paper that neural networks and knowledge-based systems must not be competitive, but are capable to complete each other. The disadvantages of the one paradigm are the advantages of the other, and vice versa. We show several ways to integrate both paradigms in the areas of explorative data analysis, knowledge acquisition, introspection, and unification. Our approach to such hybrid systems has been proved in real world applications.