Artificial intelligence

Expert systems require the assistance of an expert in order to be implemented. In addition to having access to an expert, the individual (or individuals, since more than one expert is often used) must be able to explain how they arrive at a particular decision. It is not enough for an expert to tell the interviewer what the answer to a problem is; the expert must be able to describe the reasoning behind the solution. In this way, rules can be developed and coded into the expert system for later extraction. This is a time-consuming process that can have many iterations before a successful gro

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etwork involved, but all learning methods focus on the weighted interconnections. For example, a system may have five input nodes and three output nodes (with hidden layers). The system can be taught to associated the input for "workers" with the output "earn wages." If the input for "workers" is 1-1-1-0-0 and the output for "earn wages" is 1-0-0 (each 1 representing an "on" node and each 0 representing an "off" node), the system would output "1-0-0" whenever it encounters the pattern 11100 (Zahedi, 1991, p. 29). It is possible for a neural network to link many input patterns, some of them overlapping, with associated output patterns. During operation, it recalls output patterns based on the nearest match between the learned weights and the input patterns. The ability of neural networks to produce correct outputs for input patterns that have not been previously encountered is called generalization, and neural networks must be trained for this capacity. A low generalization capability leads to inflexibility in the system with respect to small errors and small variations in input patterns. Too much generalization leads to misclassification and output errors. One of the significant drawbacks to neural network systems is
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Approximate Word count = 1971
Approximate Pages = 8 (250 words per page)

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