A Critical Examination Of Connectionist Cognitive Architectures

The dissertation represents a critical evaluation of the major connectionist theories of human cognitive architecture. The central connectionist thesis that artificial neural networks (ANNs) can serve as finitary models of human cognizers is examined and rejected. Connectionist theories, in contrast to the classical symbol-processing theories of cognitive architecture, cannot explain the productivity and systematicity of mental states. The reason for this is that ANN-based cognitive architectures cannot maintain representational states with compositional structure. Chapter One analyzes the implementational connectionism\u27s solution to the problem of compositionality. It is shown that neither the theory of weak nor of strong compositionality can solve this problem.;Chapter Two criticises the attempt to establish connectionism as an alternative theory of human cognitive architecture through the introduction of the symbolic/subsymbolic distinction. The reasons for the introduction of this distinction are examined and found to be unconvincing. Several experimental comparisons between the TDIDT class of symbolic learning systems and the class of artificial neural networks using the error backpropagation algorithm are discussed. It is argued that the differences in the performance of these two classes of learning systems are insignificant and are not systematic. Such evidence contradicts the view that ANNs define a new kind of subsymbolic computation.;Supporters of eliminative connectionism have argued for a pattern association and pattern recognition-based explanation of cognitive processes. They deny that explicit rules and symbolic representations play any role in cognition. Their argument is based to a large extent on Rumelhart and McClelland\u27s and MacWhinney and Leinbach\u27s connectionist models of learning of the past tenses of English verbs. Chapter Three presents an analysis of an experimental comparison between these models and the Symbolic Pattern Associator (SPA)--a learning system based on the classical architecture. It is shown that the SPA outperforms the connectionist models; moreover, the SPA can represent the acquired knowledge in the form of explicit rules. The analysis of this comparison leads to the conclusion that symbol-processing models have a far better chance of explaining complex cognitive phenomena in terms of rules and symbolic representations than eliminative connectionism

What is Computational Intelligence and where is it going?

What is Computational Intelligence (CI) and what are its relations with Artificial Intelligence (AI)? A brief survey of the scope of CI journals and books with ``computational intelligence'' in their title shows that at present it is an umbrella for three core technologies (neural, fuzzy and evolutionary), their applications, and selected fashionable pattern recognition methods. At present CI has no comprehensive foundations and is more a bag of tricks than a solid branch of science. The change of focus from methods to challenging problems is advocated, with CI defined as a part of computer and engineering sciences devoted to solution of non-algoritmizable problems. In this view AI is a part of CI focused on problems related to higher cognitive functions, while the rest of the CI community works on problems related to perception and control, or lower cognitive functions. Grand challenges on both sides of this spectrum are addressed