Expert systems have proven effective in a number of problem domains that
usually require human intelligence (Patterson 326). They were developed in the
research labs of universities in the 1960's and 1970's. Expert systems are
primarily used as specialized problem solvers. The areas that this can cover are
almost endless. This can include law, chemistry, biology, engineering,
manufacturing, aerospace, military operations, finance, banking, meteorology,
geology, and more. Expert systems use knowledge instead of data to control the
solution process. In knowledge lies the power is a theme repeated when building
such systems. These systems are capable of explaining the answer to the problem
and why any requested knowledge was necessary. Expert systems use symbolic
representations for knowledge and perform computations through manipulations of
the different symbols (Patterson 329). But perhaps the greatest advantage to
expert systems is their ability to realize their limits and capabilities.
Case-based reasoning (CBR) is similar to expert system because theoretically
they could use they same set of data. CBR has been proposed as a more
psychologically plausible model of the reasoning used by an expert while expert
systems use more fashionable rule-based reasoning systems (Riesbeck 9). This
type of system uses a different computational element that decides the outcome
of a given input. Instead of rules in an expert system, CBR uses cases to
evaluate each input uniquely. Each case would be matched to what a human expert
would do in a specific case. Additionally this system knows no right answers,
just those that were used in former cases to match.
A case library is set up and
each decision is stored. The input question is characterized to appropriate
features that are recognizable and is matched to a similar past problem and its
solution is then applied. Now that each type of implementation of AI has been
discussed, how do we use all this technology? Foremost, neural networks are used
mainly for internal corporate applications in various types of problems. For
example, Troy Nolen was hired by a major defense contractor to design programs
for guiding flight and battle patterns of the YF-22 fighter. His software runs
on five on-board computers and makes split-second decisions based on data from
ground stations, radar, and other sources. Additionally it predicts what the
enemy planes would do, guiding the jet's actions consequently (Schwartz 136).
Now he and many others design financial software based on their experience with
neural networks. Nolen works for Merrill Lynch & Co. to develop software that
will predict the prices of many stocks and bonds. Murry Ruggiero also designs
software, but his forecasts the future of the Standard & Poors index. Ruggiero's
program, called BrainCel, is capable of giving an annual return of 292%. Another
major application of neural networks is detecting credit card fraud. Mellon
Bank, First Bank, and Colonial National Bank all use neural networks that can
determine the difference between fraud and regular transactions (Bylinsky 98).
Mellon Bank states the new neural network allows them to eliminate 90% of the
false alarms that occur under traditional detection systems (Bylinsky 99).
Secondly, fuzzy logic has many applications that hit close to home. Home
appliances win most of the ground with AI enhanced washing machines, vacuum
cleaners, and air-conditioners. Hitachi and Matsu*censored*a manufacture washing
machines that automatically adjust for load size and how dirty the articles are
(Shine 57). This machine washes until clean, not just for ten minutes. Matsu*censored*a
also manufactures vacuum cleaners that adjust the suction power according to the
volume of dust and the nature of the floor. Lastly, Mitsubishi uses fuzzy logic
to slow air-conditioners gradually to the desired temperature. The power
consumption is reduced by 20% using this system (Schmuller 27). The chaos theory
is limited in scope at this time mainly because of lack of interest and
resources to experiment with.
