Symbolic Learning in Connectionist Production Systems

Abstract

The paper discusses an approach to combine classical artificial intelligence with connectionism. The crucial point with this intention is the implementation of symbols in neural networks. Several proposals are mentioned in [2]. The next important thing to be examined is the increase or modification of knowledge in a connectionist system. In this paper two types of learning are introduced: subsymbolic learning by experience and symbolic learning from linguistic rules. Symbolic learning requires two regions of processing, the language region and the signal region, both of them being coupled with associative links. Symbolic rules are processed sequentially in several cycles, and affect mainly the language region by also influencing the signal region, whereas subsymbolic rules are local to each of the regions and their consequences can be concluded in parallel. In a combined system with both kinds of rules subsymbolic learning and subsymbolic rules are the basis for symbolic learning and the application of linguistic rules. First results with a prototype system are introduced.