Paradigms of Artificial Intelligence

A Methodological and Computational Analysis

by Achim Hoffmann, Springer-Verlag, August 1998


ISBN 981-3083-97-2

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Table of Contents

From the back of the book

From the Preface

The field of artificial intelligence (AI), formally founded in 1956, attempts to understand, model and design intelligent systems. Since the beginning of AI, two alternative approaches were pursued to model intelligence: on the one hand, there was the symbolic approach which was a mathematically oriented way of abstractly describing processes leading to intelligent behaviour. On the other hand, there was a rather physiologically oriented approach, which favoured the modelling of brain functions in order to reverse-engineer intelligence. Between the late 1960s and the mid-1980s, virtually all research in the field of AI and cognitive science was conducted in the symbolic paradigm. This was due to the highly influental analysis of the capabilities and limitations of the perceptron by [Minsky and Papert, 1969]. The perceptron was a very popular neural model at that time. In the mid-1980s a renaissance of neural networks took place under the new title of connectionism, challenging the dominant symbolic paradigm of AI. The `brain-oriented' connectionist paradigm claims that research in the traditional symbolic paradigm cannot be successful since symbols are insufficient to model crucial aspects of cognition and intelligence. Since then a debate between the advocates of both paradigms is taking place, which frequently tends to become polemic in many writings on the virtues and vices of either the symbolic or the connectionist paradigm. Advocates on both sides have often neither appreciated nor really addressed each others arguments or concerns.
Besides this somewhat frustrating state of the debate, the main motivation for writing this book was the methodological analysis of both paradigms, which is presented in part III of this book and which I feel has been long overdue. In part III, I set out to develop criteria which any successful method for building AI systems and any successful theory for understanding cognition has to fulfill. The main arguments put forward by the advocates on both sides fail to address the methodologically important and ultimately decisive question for or against a paradigm:
How feasible is the development of an AI system or the understanding of a theory of cognition?

The significance of this question is: it is not only the nature of an intelligent system or the phenomenon of cognition itself which plays the crucial role, but also the human subject who is to perform the design or who wants to understand a theory of cognition.
The arguments for or against one of the paradigms have, by and large, completely forgotten the role of the human subject. The specific capabilities and limitations of the human subject to understand a theory or a number of design steps needs to be an instrumental criterion in deciding which of the paradigms is more appropriate. Furthermore, the human subject's capabilities and limitations have to provide the guideline for the development of more suitable frameworks for AI and cognitive science. Hence, the major theme of this book are methodological considerations regarding the form and purpose of a theory, which could and should be the outcome of our scientific endeavours in AI and cognitive science.
This book is written for researchers, students, and technically skilled observers of the rapidly evolving fields of AI and cognitive science alike. While the third part is putting forward my methodological criticism, part I and II While the third part is putting forward my methodological criticism, part I and II provide the fundamental ideas and basic techniques of the symbolic and connectionist paradigm respectively. The first two parts are mainly written for those readers, which are new to the field, or are only familiar with one of the paradigms, to allow an easy grasp of the essential ideas of both paradigms. Both parts present the kernel of each paradigm without attempting to cover the details of latest developments, as those do not affect the fundamental ideas. The methodological analysis of both paradigms with respect to their suitability for building AI systems and for understanding cognition is presented in part III.




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