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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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