Expert System

Expert System

Typically, an expert system can be termed as an advanced and computerized application that can be implemented for various purposes, such as providing clarifications to complicated problems. Additionally, an expert system can also be used in the interpretations of several uncertainties by using non-algorithmic programs that mostly depend on human expertise. Expert systems are mainly considered as broad alternatives that are used in search of various solutions that mostly require the presence of specified human expertise (Aneja, 2018).

Moreover, the expert system can also be used in justifying its solutions based on the data and knowledge from numerous past users. In this current generation, the expert systems are used primarily in making business-related strategies, and analysis of any specified business performance. Also, expert systems may be useful in the configuration of computers as well as performing complex functions that may require the presence of human expertise.

Furthermore, the difference between an expert system to a conventional problem-solving system is a latter that specifies the systems where both data and programs are encoded. Whereas, the expert system contains only hard-coded data with no specified information encoded to the program structure.

An expert system may be used in various areas such as chaining, uncertainty, data representation, user interface, providing explanations, and many more functions. In this context, I will mainly focus on two areas that involve chaining and data representation.

Chaining  

Through using the inference rules, there are two main reasoning methods to be implemented. These methods involve backward chaining and forward chaining.

Forward chaining   

This type of chaining begins on conditions whereby data is available and also the inference rules which are used in the conclusion of accumulated data until reaching the desired goals. An inference engine that uses forward chaining is used in search of various inference rules until it locates a clause that is known to be true. Later, it concludes the discovered clause then tops the received information to its data. The system continues with the above sequence until an ultimate goal is archived. Since the data available is used to determine a specified inference rule to be used, the above method can also be termed as data-driven.

Backward chaining

In this method, backward chaining begins with a list of hypotheses or goals and mainly works backward from the sequential and to the antecedent. This act is mainly to see if there is any available data that may be available in offering support. An inference engine that uses backward chaining searches the inference rules until when it locates a solution that has a consequent that is in line with their desired goals. However, in other cases where the antecedent of the rule in question is not valid, then the obtained information would be added to the goals list. For the confirmation of one’s goal, an individual must provide verified data that can be used in the acceptance of the new rule. For instance, confirmation may be provided through a Google search engine that uses backward chaining.

Data representation   

Data representation may be termed as complicated or simple, depending on the problem at hand. Mostly, some fundamental schemes are known to use attribute-value pairs. This includes size-large and color-white. In cases where a system is reasoning about manifold objects, it is essential to include both the attribute value as well as the object. For instance, a furniture placement system may be specified with chairs containing unlike attributes such as size. In such a case, the data representation must always include the objects in question for purposes of accurate results. As long as there are objects in the system, each of the objects in question may have numerous attributes. As a result, it leads to the creation of a record based structure which contains the objects name as well as all its related attributes (Hartmann et al.,2018, p. xx)

The advantages of using expert systems

As initially stated, expert systems have been mainly used in the business filed. This has mainly helped the world gain tactical benefits and forecast in the market realm. In this competitive era, decisions made in business are very crucial; hence, the assistance provided by expert systems are undoubtedly reliable and very essential for the success of an organization (Leonard and Sviokla, 2017). The advantages of using the above systems include: providing reasonable clarifications, the endowment of consistent solutions, and overcoming of various human limitations. Also, expert systems can quickly adapt to new conditions.

The disadvantages of using expert systems

Regardless of the tremendous advantages brought about by using the expert system, the act also has some drawbacks. These drawbacks include a high expense in both implementation and maintenance, lack of common sense since all decisions are mainly based on several inference rules that are encoded in the system. At times, it may provide wrong resolutions. Nevertheless, there are lots of challenges encountered when creating the inference rules to be used.

It is completely hard to measure if the advantages of using the expert system overweigh the disadvantages that come along with the implementation of the system. However, in my opinion, I regard the application of expert systems as crucial in this ever-evolving business world.

References

Aneja, A. (2018, August 2). Expert systems. GeeksforGeeks. https://www.geeksforgeeks.org/expert-systems/

Leonard, D., & Sviokla, J. (2017, March 1). Putting expert systems to work. Harvard Business Review. https://hbr.org/1988/03/putting-expert-systems-to-work

Hartmann, S., Ma, H., Hameurlain, A., Pernul, G., & Wagner, R. R. (2018). Database and expert systems applications: 29th International Conference, DEXA 2018, Regensburg, Germany, September 3–6, 2018, proceedings. Springer.