Attaching a connectionist learning assistant to inference engine

Integration of symbolism and connectionism is an important issue for building intelligent systems. It is also a challenging issue, due to the different medium of representations required for these two paradigms. In this paper the author explores the synergy of connectionism and expert systems (or, more generally, knowledge-based systems) through an expanding approach; namely, the architecture of classical expert systems is expanded by attaching a connectionist learning assistant. The connectionist learning assistant is able to learn by observing user behavior and capturing the results of observations; these results are used in conjunction with the inference engine so that flexible inference control can be achieved and the adaptability of expert systems can be enhanced. In this article, the author explains why the expanding approach is needed, why flexible control of the inference engine is important, and how this can be realized by coupling a connectionist learning component with the inference engine. These considerations result in an expanded knowledge-based system architecture, which features a flexible searching controller attached on an inference engine, a connectionist learning assistant, and a user modeller