Beyond Rationalism: Symbols, Patterns and Behavior

Abstract

Rationalism has been referred to as the tradition of explaining cognition in terms of logical structures. Much of the work in traditional AI can be seen within a rationalistic framework. Because of the problems with traditional AI, connectionist models have been proposed as an alternative. Connectionist models do solve a number of problems of AI in interesting ways, e.g. learning, generalization, and fault and noise tolerance. However, they do not automatically provide solutions to the basic conceptual problems which can be traced back to a neglect of the relation of AI systems with the real world. We will argue that if we are to make progress in the understanding of (intelligent) behavior the real issue is not whether connectionism is a better paradigm for cognitive science than traditional AI but whether a rationalistic perspective is appropriate and if not what the alternatives are. It is suggested that studying physically instantiated autonomous agents is an important step. However, we will show that building autonomous agents alone does not solve the problem either. What is needed is an appropriate embedding in a non-rationalistic framework. We will discuss a potential solution using an approach we have been developing in our group, called ‘distributed adaptive control’.