No subject

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The  field  of  artificial intelligence (AI), formally founded in
1956, attempts to understand, model and design  intelligent  sys-
tems.  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 ab-
stractly 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  or-
der  to reverse-engineer intelligence. Between the late 1960s and
the mid-1980s, virtually all research in the field of AI and cog-
nitive  science  was conducted in the symbolic paradigm. This was
due to the highly influential 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 as-
pects 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 frus-
trating 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 the-
ory  for  understanding  cognition has to fulfill. The main argu-
ments 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 cog-
nition.  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  re-
garding  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  sys-
tems and for understanding cognition is presented in part III.


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