Expert system advantages

Fekete, J. Morovjan, G. Csizmadia, F. Darvas, F. Methods development hy an expert-system—advantages and limitations. J. Chromatogr. A, 1994, 660, 33-46. 

Bayesian probability theory and methods that are based on fuzzy-set theory. The principles of both theories are explained in Chapter 16 and Chapter 19, respectively. Both approaches have advantages and disadvantages for the use in expert systems and it must be emphasized that none of the methods, developed up to now are satisfactory [7,11]. 

In these examples, the expert system is simply applying the expertise used in its development. The potential advantage of the operator advisor is that this expertise is available quickly, on any shift, for providing organized advice to the operator. 

Virtually all tasks which require the routine application of human expertise, in an organized way, are candidates for expert systems. The computer implementation of expertise has such advantages as speed, around-the-clock availability, and ease of expansion of the knowledge base. As such, expert systems represent the next generation of higher level software, performing tasks presently done by human operators. 

In such a situation an intelligent program may function as an intelligent interface, a program which can translate information from one conceptual framework to another. Even though there are many experts in the subject matter involved, programs of this type would be useful for the many others who are not expert in the subject matter or the calculations involved or who have difficulties in communication, The advent of software for small expert systems on microcomputers would add the advantage of convenience as well. 

Multilevel expert systems offer additional advantages over traditional expert systems. Multilevel expert systems draw on computational computer programs to solve parts of the problem. The Ag formulation expert system does this in the areas of computational chemistry, bookkeeping, and communication. 

The success of SYNLMA shows that it is possible to base an expert system on a theorem prover. The advantage of using a theorem prover as deductive component is that it allows us to experiment with a number of different representations for chemical information. The same flexibility makes it easy to add new starting materials and reaction rules from large commercial online databases. 

Expert systems can be developed using either conventional computer languages, special purpose languages, or with the assistance of development shells or tool-kits. Conventional languages such as PASCAL and C have the advantages of wide applicability and full flexibility to create the control and inferencing strategies required. They also are well supported and easy to customize. However, considerable amounts of time and effort are needed to create the basic facilities. 

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