Honavar, Vasant and Uhr, Leonard (1994) Symbolic Artificial Intelligence, Connectionist Networks & Beyond.. Technical Report TR94-16, Department of Computer Science, Iowa State University.
Symbolic Artificial Intelligence, Connectionist Networks, and Beyond
Department of Computer Science
226 Atanasoff Hall
Iowa State University
Ames, Iowa 50011-1040
Computer Sciences Department
1210 Dayton Street
University of Wisconsin
Madison, Wisconsin 53706
This is a preprint of an invited chapter that appears in:
Goonatilake, S. and Khebbal, S. (Ed.) Intelligent Hybrid Systems.
London: Wiley (1994).
Some of the material in this chapter overlaps with:
Honavar, V. Symbolic Artificial Intelligence and Numeric Artificial
Neural Networks: Towards a Resolution of the Dichotomy.
Sun, R. and Bookman, L. (Ed.) new York: Kluwer.
The goal of Artificial Intelligence, broadly defined, is to understand and
engineer intelligent systems. This entails building theories and models of
embodied minds and brains -- both natural as well as artificial. The advent
of digital computers and the parallel development of the theory of
computation since the 1950s provided a new set of tools with which to approach this
through analysis, design, and evaluation of computers and programs that
exhibit aspects of intelligent behavior -- such as the ability to recognize
and classify patterns; to reason from premises to logical conclusions; and to learn from experience.
The early years of artificial intelligence saw some people
writing programs that they executed on serial stored--program computers
(e.g., Newell, Shaw and Simon, 1963; Feigenbaum, 1963); Others
(e.g., Rashevsky, 1960; McCulloch and Pitts, 1943; Selfridge and Neisser, 1963; Uhr and Vossler, 1963)
worked on more or less precise
specifications of more parallel, brain--like networks of simple processors
(reminiscent of today's connectionist networks) for modelling minds/brains;
and a few took the middle ground (Uhr, 1973; Holland, 1975;
Minsky, 1963; Arbib, 1972; Grossberg, 1982; Klir, 1985).
It is often suggested that two major approaches have emerged -- symbolic
artificial intelligence (SAI) and artificial neural networks
or connectionist networks (CN) and some (Norman, 1986; Schneider, 1987) have even suggested
that they are fundamentally and perhaps irreconcilably different. Others have
argued that CN models have little to contribute to our efforts to understand
cognitive processes (Fodor and Pylyshyn, 1988). A critical
examination of the popular conceptions of SAI and CN models suggests that
neither of these extreme positions is justified (Boden, 1994; Honavar and Uhr, 1990a; Honavar, 1994b;
Uhr and Honavar, 1994). Recent attempts at reconciling SAI and CN
approaches to modelling cognition and engineering intelligent
systems (Honavar and Uhr, 1994; Sun and Bookman, 1994; Levine and Aparicioiv, 1994; Goonatilake and Khebbal, 1994; Medsker, 1994) are strongly suggestive of the potential
benefits of exploring computational models that judiciously integrate aspects
of both. The rich and interesting space of designs that combine
concepts, constructs, techniques and technologies drawn from both
SAI and CN invite systematic theoretical as well as experimental exploration
in the context of a broad range of problems in
perception, knowledge representation and inference, robotics, language,
and learning, and ultimately, integrated systems that display what might be
considered human--like general intelligence. This chapter examines how
today's CN models can be extended to provide a framework for such an exploration.
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