The Relevance of Connectionism to AI: A Representation and Reasoning Perspective

It is generally acknowledged that tremendous computational activity underlies some of the most commonplace cognitive behavior. If we view these computations as systematic rule governed operations over symbolic structures (i.e., inferences) we are confronted with the following challenge: Any generalized notion of inference is intractable, yet our ability to perform cognitive tasks such as language understanding in real-time suggests that we are capable of performing a wide range of inferences with extreme efficiency - almost as a matter of reflex. One response to the above challenge is that the traditional formulation is simply inappropriate and it is erroneous to view computations underlying cognition as inferences. An alternate response - and the one pursued in this paper - is that the traditional account is basically sound: The notion of symbolic representation is fundamental to a computational model of cognition and so is the view that computations in a cognitive system correspond to systematic rule governed operations. However, there is much more to a computational account of cognition than what is captured by these assertions. What is missing is an appreciation of the intimate and symbiotic relationship between the nature of representation, the effectiveness of inference, and the computational architecture in which the computations are situated. We argue that the structured connectionist approach offers the appropriate framework for explicating this symbiotic relationship and meeting the challenge of computational effectiveness.