Transcript Expert Systems

ICT IGCSE
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Understand the use of expert systems in
 Mineral prospecting
 Car engine fault diagnosis
 Medical diagnosis
 Oil/mineral prospecting
 Plant/animal identification
 Strategy games eg Chess
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An expert system is computer software that
attempts to act like a human expert on a
particular subject area.
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Expert systems are often used to advise nonexperts in situations where a human expert is
unavailable (for example it may be too
expensive to employ a human expert, or it
might be a difficult to reach the location).
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Diagnosing a person’s illness
Diagnostics (car engine faults, circuit board
faults etc)
Prospecting for oil and minerals
Tax and financial calculations
Chess games
Identification of plants, animals, chemical
compounds
Road scheduling for delivery vehicles
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An expert system is a knowledge-based
system which attempts to replace a human
'expert' in a particular field. The system will
consist of
 a large database of knowledge
 facilities for searching the knowledge database
 a set of rules for making deductions from the
data
 An engine to apply those rules (inference engine)
An expert system is made up of four parts:
 A user interface
 A knowledge base
 A rules base
 An inference engine
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This is the system that
allows a non-expert user
to query (question) the
expert system, and to
receive advice. The userinterface is designed to be
a simple to use as
possible.
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This is a collection of
facts. The knowledge
base is created from
information provided
by human experts.
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It is a database designed to allow the complex
storage and retrieval requirements of the expert
systems
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This is made up of a series of ‘inference rules’
 IF the country is in South America
 AND the language is Portuguese,
 THEN the country must be Brazil
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These inference rules are used by the
inference engine to draw conclusions.
The inference rules closely follow human
reasoning.
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This acts rather like a search
engine, examining the
knowledge base for information
that matches the user's query.
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It is software that attempts to
derive answers from the
knowledge base using a form
of reasoning.
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The non-expert user queries
the expert system. This is
done by asking a question,
or by answering questions
asked by the expert system.
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The inference engine uses
the query to search the
knowledge base and then
provides an answer or some
advice to the user.
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Medical diagnosis (the
knowledge base would
contain medical
information, the symptoms
of the patient would be
used as the query, and the
advice would be a diagnose
of the patient’s illness)
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Playing strategy games
like chess against a
computer (the knowledge
base would contain
strategies and moves, the
player's moves would be
used as the query, and the
output would be the
computer's 'expert' moves)
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Providing financial
advice - whether to
invest in a business, etc.
(the knowledge base
would contain data
about the performance
of financial markets and
businesses in the past)
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Helping to identify items
such as plants / animals /
rocks / etc. (the knowledge
base would contain
characteristics of every item,
the details of an unknown
item would be used as the
query, and the advice would
be a likely identification)
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Helping to discover locations
to drill for water / oil (the
knowledge base would
contain characteristics of likely
rock formations where oil /
water could be found, the
details of a particular location
would be used as the query,
and the advice would be the
likelihood of finding oil / water
there)
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Helping to diagnose car
engine problems (like
medical diagnosis, but for
cars!)
Two types:
 Onboard
 In workshops
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Human experts make mistakes all the time
(people forget things, etc.) so you might
imagine that a computer-based expert
system would be much better to have around.
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An ES can store far more
information than a human.
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Expert systems provide consistent
answers.
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ES does not 'forget’ to ask
important questions, or make
mistakes.
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Reduces the time taken to solve a
problem
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Data can be kept up-to-date.
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Data can be collected from many
experts
The expert system is always
available 24 hours a day and will
never 'retire'.
The system can be used at a
distance over a network.
A less skilled workforce is needed
Opportunity to save money
People/areas can access expertise they couldn’t
otherwise afford
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Very expensive to set up in the
first place
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The computer’s reasoning is only
as good as the rules it has been
given
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They have no 'common sense'
(a human user tends to notice
obvious errors, whereas a
computer wouldn't)
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Errors in the knowledge base can lead to incorrect
decisions being made
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Mistakes made when entering facts/answers to
questions can lead to incorrect decisions
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Not possible to break ALL expert knowledge into
facts, rules & probabilities
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No human interaction/human touch
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Considerable training is required to ensure the
system is used correctly by the operators