AI programs solve problems by reasoning from first principles. They can explain their reasoning by reporting the string of deductions that led from the input data to the conclusion, with the Human Experts.
An expert encountering a new problem is usually reminded of similar cases seen in the past, remembering the result of those cases and perhaps the reasoning behind. those results.
Medical expertise follow this pattern. Computer systems that solve new problems by analogy with old ones are often called Case Base Reasoning (CBR).
A successful CBR systems must answer the following questions.
1. How are cases organized in memory ?
2. How are relevant cases retrieved from the memory ?
3. How can previous cases be adopted to new problems ?
4. How are cases originally acquired ?
The memory structures we discussed in the previous section are clearly relevant to CBR. The use of memory effectively, we must have a rich indexing mechanism. When we are presented with a problem we should be reminded of relevant past experience. Important features are not always the most obvious ones.
X described how his wife would never cook his steak as rare as he like it. When X told this to Y, Y was reminded of a time. 30 years earlier, when he tried to get his hair cut in England and the barber just would not cut it as short as he wanted it.
Clearly the indices steak, wife and Rare are insufficient to remind Y of the barbershop episode.
Some features are only important in certain contexts.