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AN INTRODUCTION TO HYBRID

CONNECTIONIST-SYMBOLIC

MODELS

Ron Sun

Department of Computer Science
The University of Alabama

1 MOTIVATIONS

There has been a considerable amount of research in integrating connectionist and symbolic processing. While such an approach has clear advantages, it also encounters serious difficulties and challenges. Consequently, various ideas and models have been proposed to address different problems and different aspects in this integration. The need for such models has been slowly but steadily growing over the past five years, from many segments of the artificial intelligence and cognitive science communities, ranging from expert systems to cognitive modeling and to logical reasoning. Some interesting and important approaches have been developed. There has been a general consensus that hybrid connectionist-symbolic models constitute a promising avenue toward developing more robust, more powerful, and more versatile architectures, both for cognitive modeling and for intelligent systems. It is definitely worthwhile pursuing research in this area further still, which might generate important new ideas and significant new applications.

The basic motivations for research in hybrid connectionist-symbolic models can be briefly summarized as follows:

Cognitive processes are not homogeneous; a wide variety of representations and mechanisms are employed. Some parts of cognitive processes are best captured by symbolic models, while others by connectionist models (Smolensky 1988, Sun 1995). Therefore, a need for pluralism" exists in cognitive modeling, which leads to the development of hybrid models as tools and frameworks.

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