Computer Brain--Indexing

[ Expert Systems ]

n psychology, the paradox of the expert states that the more one knows, the harder it is for one to recall any specific information.  One would think that the amount of knowledge an old man has acquired would take him a very long time to sort through his life-time of wisdom to figure out why his car won't start one morning.  In reality, this is not the case.   Assuming that he has some knowledge of cars, that wise man would probably be able to diagnose his car's problem in a couple of minutes and be on his way.  How does he figure out what to check or try out to see what was wrong with it when such a complicated thing as a car could have a multitude of things that could go wrong?  When asked the same question, he said, "I just systematically checked certain things in a certain order.  First, I looked to see if there's any gas in the fuel tank.  If there is, then maybe there's a problem with the spark plug so I check that next..."

The wise man's car example illustrated one of the ways to make decisions on what knowledge to actually access.  The man had certain rules on what to check that prevented him from being overwhelmed by all the possible reasons that won't prevent his car from starting.  Controlling decision-making processes is another concern in AI and has been successful in employing this type of method in what is known as expert systems to prevent a computer from being lost in a sea of information. 

Expert Systems

As the name implies, expert systems are designed to replicate the decision-making processes of human experts in a particular field.  As a combination of a knowledge base, decision rules, and an inference engine, an expert system uses an elaborate set of rules that it can infer knowledge from one to another.  For the example of the non-starting car, an expert system from Ford's Service Bay Diagnostic System would go through a set of rules: