Honavar, Vasant (1994) Symbolic Artificial Intelligence and Numeric Artificial Neural Networks: Towards A Resolution of the Dichotomy. Technical Report TR94-14, Department of Computer Science, Iowa State University.
Symbolic Artificial Intelligence and Numeric Artificial Neural Networks:
Towards a Resolution of the Dichotomy
Department of Computer Science
Iowa State University
226 Atanasoff Hall
Ames, IA 50011-1040
Tech Report 94-14
This is a preprint of an invited chapter that appears in:
Sun, R. and Bookman, L. (Ed.) Computational Architectures Integrating
Symbolic and Neural Processes. New York: Kluwer (1994).
The attempt to understand intelligence entails building theories and models
of brains and minds, both natural as well as artificial. From the earliest
writings of India and Greece, this has been a central problem in philosophy.
The advent of the digital computer in the
1950's made this a central concern of computer scientists as well (Turing, 1950). The parallel development of the theory of computation (by John
von Neumann, Alan Turing, Emil Post, Alonzo Church, Charles Kleene, Markov
and others) provided a new set of tools with which to approach this problem
--- 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.
In their pursuit of artificial intelligence and mind/brain modelling, some
wrote programs that they executed on serial stored---program computers
(e.g., Newell, Shaw and Simon, 1963; Feigenbaum, 1963); Others had
more parallel, brain---like networks of processors (reminiscent of today's
connectionist networks) in mind and wrote more or less precise
specifications of what such a realization of their programs might look like
(e.g., Rashevsky, 1960; McCulloch and Pitts, 1943; Selfridge and Neisser, 1963; Uhr and Vossler, 1963); 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 (numeric) artificial neural networks
(NANN or connectionist networks) and some (Norman, 1986; Schneider, 1987) have even suggested
that they are fundamentally and perhaps irreconcilably different. Indeed it
is this apparent dichotomy between the two apparently disparate approaches
to modelling cognition and engineering intelligent systems that is
responsible for the current interest in computational architectures for
integrating neural and symbolic processes. This topic is the focus of several
recent books (Honavar and Uhr, 1994a; Goonatilake and Khebbal, 1994;
Levine and Aparicioiv, 1994; Sun and Bookman, 1994). This raises some
important questions: What exactly are symbolic processes? What do they have
to do with SAI? What exactly are neural processes? What do they have to do
with NANN? What (if anything) do SAI and NANN have in common?
How (if at all) do they differ?
What exactly are computational architectures?
Do SAI and NANN paradigms need to be integrated?
Assuming that the answer to the last question is yes, what are some possible
ways one can go about designing computational architectures for this task?
This chapter is an attempt to explore
some of these fundamental questions in some detail.
This chapter argues that the dichotomy between SAI and NANN is more perceived
than real. So our problems lie first in dispelling misinformed and wrong
notions, and second (perhaps more difficult) in developing systems that take
advantage of both paradigms to build useful theories and models of
minds/brains on the one hand, and robust, versatile and adaptive intelligent
systems on the other. The first of these problems is best addressed by a
critical examination of the popular conceptions of SAI and NANN systems
along with their philosophical and theoretical foundations as well as their
practical implementations; and the second by a judicious theoretical
and experimental exploration of the rich and interesting space of designs
for intelligent systems
that integrate concepts, constructs, techniques and technologies drawn from
not only SAI (Ginsberg, 1993; Winston, 1992) and NANN (McClelland, Rumelhart et al., 1986; Kung, 1993; Haykin, 1994; Zeidenberg, 1989),
but also other related paradigms such as statistical
and syntactic pattern recognition (Duda and Hart, 1973; Fukunaga, 1990; Fu, 1982; Miclet, 1986), control theory (Narendra and Annaswamy, 1989) systems theory (Klir, 1969), genetic algorithms (Holland, 1975; Goldberg, 1989; Michalewicz, 1992) and evolutionary programming (Koza, 1992). Exploration of such designs should
cover 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.
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