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428 Alternative decision-support systems
(1) Voice/image recognition. The aim here is to produce systems that
can recognize verbal instructions and visual images, such as the
changing view from the driving seat of a car. As yet, systems can
only recognize a few score of spoken words which are given as
one-word commands by an individual whose voice patterns have
been matched by the computer. In addition, computer systems have
been developed that can recognize simple images such as typewriter
typeface print, but a system that can perform the same voice- and
image-recognition functions as a copy typist may be an unrealizable
dream. Consider, for example, the problems involved in producing a
typewritten letter from verbal/written instructions for a person the
typist has never met before – the recognition of an unfamiliar voice
and/or handwriting and of the person’s face when he or she calls to
collect the completed work would confound any computer system
currently envisaged.
(2) Robotics. While applications are now increasingly common in manufacturing,
for example, the ultimate objective of robotics is to produce
machines that ‘think’ and ‘act’ like humans. Recall, for instance, the
intelligent thoughts and actions of C3PO and the other Star Wars
robots. If producing a copy-typing system, such as the one mentioned
above, is known to present unsurmountable difficulties to
any conceivable computer system based on known technology, then
robotic research to produce machines with the flexibility of people
presents even more complex problems to the AI researcher.
Early research on expert systems was also focused on relatively complex
problems such as diagnosing the disease from which a person is
suffering. The aim was for the system to perform the diagnosis in the
same way as an expert physician. However, diagnosis turned out to be
a difficult problem, and even now, after many person-years of effort,
none of the systems built are in routine use. One reason for this is that
the systems were developed by academics who were more interested
in producing academic papers to further their careers. Relatively simple
practical problems that can be solved easily hold no challenges (or
publications) and so tend to be avoided by university-based researchers.
More recently, commercial advantage has been seen in picking the ‘lowhanging
fruit’, and it is these expert systems, built in person-months
rather than person-years, that form the focus of this section. As we shall
see, they are often targeted on a particular area of expertise.
Several definitions of an expert system have been proffered, but
the Expert Systems group of the British Computer Society provides a