Papers presented in the area of approximate reasoning contained new methods such as fuzzy entropy method, linear revising method, approximate reasoning method with certainty factor, and Neural Networks based methods. Although Neural Networks and Fuzzy systems can be investigated separately, the majority of papers focused on the fusion of the two technologies in order to tackle more complex problems, and hence to create more intelligent machines. So far, empirical evidence is convincing for more research in this direction. In my view, the variety of design techniques is due to a lack of firm theoretical foundation. However, theoretical results are already useful as general guide lines, namely specifying the structures of Neural Networks and fuzzy systems, the classes of inference procedures used in approximate reasoning.
More basic research is needed in order to provide foundations for design methodology.
In the near future, we will continue to see more applications of the fuzzy approach to the so-called friendly systems. It was apparent that the Asian scientists pursued more research than US scientists in the fields of chaotic fuzzy systems and fuzzy measures and integrals for decision-making. The chaos in general systems results from non-linear dynamical systems and exhibits complicated and unpredictable behavior. Applications are considered for control problems, especially for systems sensitive to oscillations and chaostic instability. Dealing with uncertainty in humanistic systems is a crucial problem.