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Management Support Systems
Notes 14.2.8 Problems with Expert Systems
On the technical side, there is the problem of the size of the database and using it efficiently.
If the system consists of several thousand rules, it takes a very powerful control program to
produce any conclusions in a reasonable amount of time. If the system also has a large quantity
of information in the working memory, this will also slow things down unless you have a very
good indexing and search system.
A second problem that comes from a large database is that as the number of rules increases the
conflict set also becomes large so a good conflict resolving algorithm is needed if the system is
to be usable.
Another problem that appears is that of responsibility.
Example: A system used by a doctor that is designed to administer drugs to patients
according to their needs and that it must first determine what is wrong with them, very much
like the prescribing work of a GP. If the system causes someone to take the wrong medicine and
the person is harmed, who is legally responsible? Some would say the health authority who
allowed the doctor to use the system, others would say the doctor, others the suppliers of the
Expert System. A problem is produced that is not at all a trivial one. Think about the implications
of using Expert Systems in other scenarios.
A more obvious problem is that of gathering the rules. Human experts are expensive and are not
extremely likely to want to sit down and write out a large number of rules as to how they come
to their conclusions. More to the point, they may not be able to. Although they will usually
follow a logical path to their conclusions, putting these into a set of IF ... THEN rules may
actually be very difficult and maybe impossible.
It is quite possible that many human experts, though starting off in their professions with a set
of rules, learn to do their job through experiential knowledge and ‘just know’ what the correct
solution is. Again they may have followed a logical path, but mentally they may have ‘skipped
some steps’ along the way to get there. An Expert System cannot do this and needs to know the
rules very clearly.
What may be a way round this problem is to enable Expert Systems to learn as they go, starting
off with a smaller number of rules but given the ability to deduce new rules from what they
know and what they ‘experience’. This leads us very nicely into the field of Computer Learning.
14.2.9 Limitations of Expert System
However, Expert Systems suffer from following limitations:
Common sense: In addition to a great deal of technical knowledge, human experts have
common sense. It is not yet known how to give expert systems common sense.
Creativity: Human experts can respond creatively to unusual situations whereas expert
systems cannot.
Learning: Human experts automatically adapt to changing environments; expert systems
must be explicitly updated. Case-based reasoning and neural networks are methods that
can incorporate learning.
Sensory Experience: Human experts have available to them a wide range of sensory
experience; expert systems are currently dependent on symbolic input.
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