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1806
Chapter 6.2
Computational Intelligence
Applications in Business:
A Cross-Section of the Field
Kevin E. Voges
University of Canterbury, New Zealand
Nigel K. Ll. Pope
Griffith University, Australia
ABstrAct
We present an overview of the literature relating
to computational intelligence (also commonly
called artificial intelligence) and business applications, particularly the journal-based literature.
The modern investigation into artificial intelligence started with Alan Turing who asked in
1948 if it would be possible for “machinery to
show intelligent behaviour.” The computational
intelligence discipline is primarily concerned
with understanding the mechanisms underlying
intelligent behavior, and consequently embodying these mechanisms in machines. The term
“artificial intelligence” first appeared in print in
1955. As this overview shows, the 50 years of
research since then have produced a wide range
of techniques, many of which have important
implications for many business functions, including finance, economics, production, operations,
marketing, and management. However, gaining
access to the literature can prove difficult for
both the computational intelligence researcher
and the business practitioner, as the material is
contained in numerous journals and discipline
areas. The chapter provides access to the vast and
scattered literature by citing reviews of the main
computational intelligence techniques, including
expert systems, artificial neural networks, fuzzy
systems, rough sets, evolutionary algorithms, and
multi-agent systems.
INtroductIoN
Although its intellectual roots can be traced back to
Greek mythology (McCorduck, 2004), the modern
investigation into artificial intelligence started at
the beginning of the computer era, when Alan
Turing (1948, 1950) first investigated the question
“as to whether it is possible for machinery to show
intelligent behaviour” (Turing, 1948, p. 1). Many of
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Computational Intelligence Applications in Business
Turing’s insights in that remarkable (unpublished)
1948 manuscript became central concepts in later
investigations of machine intelligence. Some of
these concepts, including networks of artificial
neurons, only became widely available after reinvention by other researchers. For those new to
the field, there are many excellent introductions
to the study of computational intelligence (Callan,
2003; Engelbrecht, 2002; Hoffmann, 1998; Konar,
2000; Luger & Stubblefield, 1998; Munakata,
1998; Negnevitsky, 2002; Poole, Mackworth, &
Goebel, 1998).
Artificial intelligence can be defined as “the
scientific understanding of the mechanisms
underlying thought and intelligent behavior and
their embodiment in machines” (American Association for Artificial Intelligence, n.d.). The
term “artificial intelligence” first appeared in
print in 1955, in conjunction with a research program at Dartmouth College (McCarthy, Minsky,
Rochester, & Shannon, 1955). Recently the term
“computational intelligence” has been proposed
as more appropriate for this field of study (Poole
et al., 1998). As they state, “[t]he central scientific
goal of computational intelligence is to understand
the principles that make intelligent behavior possible, in natural or artificial systems” (Poole et
al., 1998, p. 1).
Poole et al. (1998) feel that “artificial intelligence” is a confusing term for a number of reasons:
artificial implies “not real,” but the field of study
looks at both natural and artificial systems; artificial also “connotes simulated intelligence” (p. 2),
but the goal is not to simulate intelligence, but to
“understand real (natural or synthetic) intelligent
systems by synthesizing them” (p. 2). As they
state: “[a] simulation of an earthquake isn’t an
earthquake; however, we want to actually create
intelligence, as you could imagine creating an
earthquake. The misunderstanding comes about
because most simulations are now carried out on
computers. However … the digital computer, the
archetype of an interpreted automatic, formal,
symbol-manipulation system, is a tool unlike any
other: It can produce the real thing” (p. 2). Computational intelligence also has the advantage of
making the “computational hypothesis explicit in
the name” (p. 2). For these reasons, we prefer (and
use) the term computational intelligence (CI).
Debates about terminology aside, 50 years of
study into “the principles of intelligent behavior”
have led to the development of a wide range of
software tools with applications relevant for
most business disciplines. The chapter provides
references to the many reviews of CI applications
available in the literature. This cross-section of
the field (as opposed to a comprehensive review)
will briefly outline some of the different “tools
of intelligence” and show examples of their applications across a broad spectrum of business
applications.
tools oF INtEllIgENcE
The study of computational intelligence has led
to a number of techniques, many of which have
had immediate practical applications, even though
they fall far short of the type of intelligent behavior
envisaged by early enthusiastic artificial intelligence practitioners and popular fiction. Some of
the CI techniques derive from abstract systems
of symbol processing (e.g., frame-based systems,
rule-based systems, logic-based systems, the
event calculus, predicate calculus, fuzzy logic,
and rough sets). More recent techniques have
emulated natural processes (e.g., neural networks,
evolutionary algorithms, auto-immune systems,
ant colony optimisation, and simulated annealing). Just to add to the confusion of terminology,
some of these latter techniques are also referred
to as “soft computing” (Tikk, Kóczy, & Gedeon,
2003). In addition, a specific sub-branch of CI is
referred to as machine learning (Flach, 2001).
This section provides a brief overview of some of
these tools of intelligence, with references to the
literature for those readers interesting in pursuing
some of the techniques in depth. The next section
1807
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