This type of system is inherently an excellent design for any application
that requires little human intervention and that must learn on the go. Created
by Lotfi Zadeh almost thirty years ago, fuzzy logic is a mathematical system
that deals with imprecise descriptions, such as new, nice, or large (Schmuller
14). This concept was also inspired from biological roots. The inherent
vagueness in everyday life motivates fuzzy logic systems (Schmuller 8). In
contrast to the usual yes and no answers, this type of system can distinguish
the shades in-between. This system provides a smart light that can decide
whether a traffic light should be changed more often or remain green longer. In
order for these smart lights to work the system assigns a value to an input and
analyzes all the inputs at once. Those inputs that have the highest value get
the highest amount of attention. Another promising arena of AI is chaos
engineering. The chaos theory is the cutting-edge mathematical discipline aimed
at making sense of the ineffable and finding order among seemingly random events
(Weiss 138). The theory came to life in 1963 at the Massachusetts Institute of
Technology. Edward Lorenz, who was frustrated with weather predictions noted
that they were inaccurate because of the tiny variations in the data. Over time
he noticed that these variations were magnified as time continued. His work went
unnoticed until 1975 when James Yorke detailed the findings to American
Mathematical Monthly. Yorke's work was the foundation of the modern chaos theory
(Weiss 139).
The theory is put into practice by using mathematics to model complex natural
phenomena. A few more implementations of artificial intelligence include
knowledge-based systems, expert systems, and case-based reasoning. All of these
are relatively similar because they all use a fixed set of rules.
Knowledge-based systems (KBS) are systems that depend on a large base of
knowledge to perform difficult tasks (Patterson 13). KBS get their information
from expert knowledge that has been programmed into facts, rules, heuristics and
procedures. 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. Case-based reasoning (CBR) is similar to expert system
because theoretically they could use the 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
(Patterson 329). This type of system uses a different computational element that
decides the outcome of a given input. Making recommendations on which AI systems
work the best almost requires AI itself. Neural networks, unfortunately, have
performance spectrums that continue to dwell at both extremes.
