An expert system is a computer program that designs to emulate and mimic human intelligence, skills, or behavior. An expert system an advanced computer application that implements to provide solutions to complex problems or to clarify uncertainties through the use of non-algorithmic programs where normally human expertise will need. Advantages and disadvantages of Expert Systems; They design to solve complex problems by reasoning through bodies of knowledge, represented mainly as if-then rules rather than through conventional procedural code. Expert systems are most common in the complex problem domain and consider as widely use alternatives in searching for solutions that require the existence of specific human expertise.
Here explain the advantages and disadvantages of expert systems.
Expert systems are most common in the complex problem domain and consider as widely use alternatives in searching for solutions that require the existence of specific human expertise. The expert system is also able to justify its provided solutions based on the knowledge and data from past users. Normally expert systems use in making business marketing strategic decisions, analyzing the performance of real-time systems, configuring computers, and perform many other functions that normally would require the existence of human expertise.
The difference between an expert system with a normal problem-solving system is that the latter is a system where both programs and data structures encode; while for an expert system only the data structures hard-coded and no problem-specific information encodes in the program structure. Instead, the knowledge of human expertise capture and codify in a process known as knowledge engineering.
Hence, whenever a particular problem requires the assistance of certain human expertise to provide a solution; the human expertise which has been codified will use and process to provide a rational and logical solution. This knowledge-based expert system enables the system to frequently add new knowledge; and, adapt accordingly to meet new requirements from the ever-changing and unpredictable environment.
Advantages of Using Expert System:
An expert system has been reliably used in the business world to gain tactical advantages and forecast the market’s condition. In this globalization era where every decision made in the business world is critical for success; the assistance provided from an expert system is undoubtedly essential and highly reliable for an organization to succeed.
Examples given below will be the advantages for the implementation of an expert system in business:
1] Providing consistent solutions:
It can provide consistent answers for repetitive decisions, processes, and tasks. As long as the rule base in the system remains the same, regardless of how many times similar problems are being tested, the conclusions drawn will remain the same.
2] Provides reasonable explanations:
It can clarify the reasons why the conclusion was drawn and be why it considers as the most logical choice among other alternatives. If there are any doubts in concluding a certain problem; it will prompt some questions for users to answer to process the logical conclusion.
3] Overcome human limitations:
It does not have human limitations and can work around the clock continuously. Users will be able to frequently use it in seeking solutions. The knowledge of experts is an invaluable asset for the company. It can store the knowledge and use it as long as the organization needs it.
4] Easy to adapt to new conditions:
Unlike humans who often have trouble adapting to new environments, an expert system has high adaptability and can meet new requirements in a short period. It also can capture new knowledge from an expert and use it as inference rules to solve new problems.
Disadvantages of Using Expert System:
Although the expert system does provide many significant advantages, it does have its drawbacks as well.
Examples given below will be the disadvantages for the implementation of an expert system in business:
1] Lacks common sense:
It lacks common sense needed in some decision making since all the decisions made base on the inference rules set in the system. It also cannot make creative and innovative responses as human experts would in unusual circumstances.
2] High implementation and maintenance cost:
The implementation of an expert system in business will be a financial burden for smaller organizations since it has high development costs as well as the subsequent recurring costs to upgrade the system to adapt to the new environment.
3] Difficulty in creating inference rules:
Domain experts will not be able to always explain their logic and reasoning needed for the knowledge engineering process. Hence, the task of codifying out knowledge is highly complex and may require high
4] May provide wrong solutions:
It is not error-free. There may error occur in the processing due to some logical mistakes made in the knowledge base, which will then provide the wrong solutions.
Classified as Expert System:
A good expert system expects to grow as it learns from user feedback. Feedback incorporates into the knowledge base as appropriate to make the expert system smarter. The dynamism of the application environment for expert systems base on the individual dynamism of the components.
This can classify as follows:
Working memory. The contents of the working memory, sometimes called the data structure, changes with each problem situation. Consequently, it is the most dynamic-component of an expert system, assuming, of course, that it keeps current.
The knowledge base need not change unless a new piece of information arises that indicates a change in the problem-solving procedure. Changes in the knowledge base should carefully evaluate before being implemented. In effect, changes should not be based on just one consultation experience. For example, a rule that is found to be irrelevant less than one problem situation may turn out to be crucial in solving other problems.
Inference engine. Because of the strict control and coding structure of an inference engine, changes make only if necessary to correct a bug or enhance the inferential process. Commercial inference engines, in particular, change only at the discretion of the developer. Since frequent updates can be disruptive and costly to clients, most commercial software developers try to minimize the frequency of updates.