An Approach to Integrating Expert Systems with Connectionist Networks

Conventional expert systems as well as connectionist systems suffer from several problems if they are used as isolated paradigms. However, in combination many of the advantages of each can be realized in one system. An example of such a “hybrid” system for the domain of high-school physics problems is presented. It is demonstrated how this system incrementally incorporates experience by compiling rule-based knowledge into a connectionist network. The problems of interfacing a symbolic with a connectionist system and the fundamental issues underlying incremental learning are discussed and illustrated by simulation experiments. It is also shown that a hybrid system not only leads to an increase in performance, but that the approach has a number of essential benefits as a research strategy, both for expert systems and connectionist networks. The results are encouraging and warrant application to a real-life domain.