A key distinction between the traditional system as opposed to the expert system is the way in which the problem related expertise is coded. Essentially, in conventional applications, the problem expertise is encoded in both program as well as data structures. On the other hand, in expert systems, the approach of the problem related expertise is encoded in data structures only. Moreover, the use of knowledge in expert systems is vital. However, traditional systems use data more efficiently than the expert system.
One of the biggest limitations of conventional systems is that they are not capable of providing explanations for the conclusion of a problem. That is because these systems try to solve problems in a straightforward manner. However, expert systems are capable of not only providing explanations but also simplifying the understanding of a particular conclusion.
Generally, an expert system uses symbolic representations to perform computations. On the contrary, conventional systems are incapable of expressing these terms. They only simplify the problems without being able to answer the “how” and “why” questions. Moreover, the problem-solving tools are present in expert systems as opposed to the traditional ones, and hence, various types of problems are most often entirely solved by the experts of the system.
|Human Experts||Expert Systems|
|Perishable and unpredictable in nature||Permanent and consistent in nature|
|Difficult to transfer and document data||Easy to transfer and document data|
|Human expert resources are expensive||Expert Systems are cost effective Systems|