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What Are Expert Systems And What Are Their Uses?

An Expert System Is A Type Of Intelligent Software That Uses Information, Or More Precisely, Knowledge Stored In A Knowledge Base, Solve Problems.

Simply put, expert systems store human knowledge in their databases. These systems can provide users with suggestions or explanations on achieving a result (product or service selection).

These systems are mainly designed and used to solve complex problems. Expert systems store information in the form of facts and rules in special databases, which are called knowledge bases and are structured, and then provide the results needed by experts with the help of inferential methods of receiving data.

Typically, an expert system consists of an argument engine, knowledge base, user interface, and knowledge acquisition module.

What is an expert system?

Expert system is one of the main branches of artificial intelligence that tries to provide specialized services like humans by gathering specialized knowledge and expert information in a specific field and applying logic. To be more precise, expert systems are intelligent software with the specialized knowledge of experts in the form of scientific information and use them. An expert system uses human knowledge to solve problems that require intelligence.

The data contained within these systems is called expert knowledge and is used to provide detailed recommendations. Expert systems are mainly used to express arguments and use certain logical patterns.

 This approach is similar to what humans do when solving a problem.

Just as humans think to solve a problem, expert systems employ patterns and algorithms designed by experts.

Until the beginning of the ’80s, there was not much activity in building and creating expert systems by artificial intelligence researchers. Since then, much work has been done in small expert systems and large expert systems in this area. In the 70’s Edward

At Stanford University, Figenbum sought to find a way to solve a problem that was not universal. Instead, the researchers found that a specialist has many specific tips and tricks for his work and, in fact, uses a set of useful tricks and rules of thumb in his work. These findings paved the way for the creation of the concept we know today as the expert system.

Expert systems try to be inspired by experts’ rules and to present flawless arguments. 

Expert systems are used in various fields, the most important of which are medicine, accounting, process control, human resources, financial services, GIS, accounting, medical financial analysis (diagnosis), angiography, and silicon wafer production.

In each of these areas, tasks such as guiding, processing, categorizing, consulting, designing, diagnosing, exploring, predicting, conceptualizing, identifying, justifying, learning, managing, planning, scheduling, and experimenting can be accomplished with the help of experimental systems. And did it more easily. Expert systems are used either as a replacement for an expert or as an assistant.

Expert system components

Typically, an expert system comprises four main components. Other components are added to these systems; however, an expert system consists of components of the Knowledge Base, Inference Engine, and explanatory features. (Explanation Facilities) and user interface (User Interface) is created.

Knowledge Base

A place, or more precisely a repository, analyzed and ready-to-use data (expert knowledge) is stored in an encoded form that is understandable to the system.

The knowledge base consists of two main components of the object, representing the result that it presents based on rules. First, an attribute describes the properties and attributes and allows the developer to define them correctly.

Based on this definition, a knowledge base can be described as a list of objects in which the rules and attributes of each object are listed. For example, the policy or rule applied to an index states whether or not the object has an index in its simplest form.

A person who prepares expert knowledge in coded form is called a knowledge engineer. In general, knowledge is stored in the form of conditional expressions and rules in the knowledge database.

Inference Engine

When representing the range of knowledge with policies, an expert still needs to determine what policies should solve a particular problem. In addition, it must be specified in which set or category the policies should be used. Accordingly, an expert system must decide which law, in what case, and in what category should be selected for evaluation.

The beating heart inference engine is an expert system because it allows the system to correctly use defined policies defined as a set of “if……” rules to find the final answer or judgment.

The factor that makes a system an expert system is how the inference engine processes the rules. The inference engine for decision-making can work in two ways. The first is to go to the data processing and extract the final result. More precisely, considering the data related to the subject in question, start from (ifs) and arrive at appropriate results or (s).

In other words, in the decision-making chain from the preparations to the results.

In the second method, first look for the initial and appropriate conditions.

And In this method, the starting points are (after), and it reaches (if). So the first method is data-driven inference and the second method is method-based.

Knowledge Engineer: A person who designs and creates an expert system. A knowledge engineer has complete mastery of intelligent algorithms and can use various intelligent patterns to solve real problems.

Explanation Facilities

Explanatory features are used to show the conclusions of the expert system for a particular issue with specific facts in a language understandable to the user. Explanatory features have the advantage that the user has more confidence in the decision made by the system by seeing the inference steps.

Database: The database collects data on topics and events used in the knowledge database to achieve the desired results.


A user interface is a set of equipment and software that acts as a communication channel between the user and the expert system. More precisely, it allows the user to provide information about the issue to the system and, on the other hand, provides the system with the conclusions of the system.

The user interface of an expert system must have a great power of exchange so that the structure of information exchange is done in an understandable dialogue for the system and the user.

Knowledge acquisition: The knowledge acquisition component manages the process, extraction, design, and presentation of knowledge.

User: A person who interacts with the system. Users of an expert system are divided into different groups and use an expert system in different ways.

What are the benefits of an expert system?

The purpose of designing and implementing an expert system is to save money and make better and more accurate decisions. However, expert systems are mostly used for making complex decisions because, in most cases, they evaluate various technical aspects that traditionally require a lot of time to process and analyze this information.

Accordingly, the efficiency of an expert system depends on the availability and ease of working with it.

The benefits of expert systems can be categorized as follows:
  • Increase reliability: If the information within the knowledge base of an expert system is reliable, the decisions it suggests can be cited.
  • Risk reduction: The expert system can be implemented in environments that may be dangerous to humans—for example, control of inlet and outlet pressure in dams.
  • Increase accessibility and reduce costs: Computers store a variety of information. An expert system is designed and implemented with the aim of mass-producing experiences.
  • Stability: Expert systems are stable and maintain their performance when their information is updated.
  • Hybrid experiences: An expert system can suggest decisions that may require several experts to make those suggestions.
  • Explanation power and quick response: An expert system can describe the reasoning process and steps leading to the conclusion. Expert systems can provide accurate results in the shortest time. Accordingly, offer the best offer in emergencies when stress harms people’s decisions.
  • Ease of knowledge transfer: One of the most important advantages of the expert system is its ease of transfer to various geographical locations.
However, expert systems are not without flaws and have their own limitations.

For example, these systems have no sense of what they are doing. In addition, they do not have the ability to generalize their expertise extensively because they are designed for a specific purpose only, and their knowledge base is based on the knowledge of the experts who designed them.

Given that experts powers expert systems, they will not analyze the new conditions accurately if unforeseen circumstances arise.

In what areas are expert systems used?

Expert systems, like other areas of artificial intelligence, have their own applications. These applications include the following:

  • The field of public services: Expert systems allow applying applied knowledge to a wider and less costly population.
  • Accounting and Finance: One of the most prosperous applications of expert systems is financial analysis. Expert systems in the field of finance Possibility of risk assessment, preparation of audit program, providing technical assistance, detection and prevention of fraud, pricing of products and services, pricing, design of accounting systems, capital budgeting, selection of accounting methods, the credit assessment, recommendations Provide taxation, calculation of tax inconsistencies, and personal financial planning.

The difference between expert systems and other information systems

Expert systems, unlike information systems that operate on data, focus on knowledge. In addition, they can use different types of numerical, symbolic, and comparative data in an inference process.

Another characteristic of these systems is the use of heuristic methods instead of algorithmic methods. This capability provides a wide range of functions for expert systems.

The conclusion process in expert systems is based on inductive and deductive methods. But, on the other hand, these systems can explain their reasons for reaching a specific conclusion or the direction and direction of their movement towards the goal.

What are the features of a good expert system?

In an expert system, an attempt is made to separate the data from the procedures implemented on the data. The advantage of this separation is that the generalization in the system increases.

The most important features that show a properly implemented expert system should be the following:
  •  Have expert and specialized knowledge, focus on specific specialties, reasoning with symbols, heuristic and empirical reasoning, inaccurate reasoning based on probabilistic reasoning (the expert system should be able to work in environments where inaccurate information or information are not perfect to argue.), The suitability of the expert system in terms of complexity (expert system issues should not be too hard and not too easy.) It warns of this in various ways (similar to what Facebook does, marking posts whose information is suspicious with unreliable labels).

last word

An expert system is an intelligent program designed to model a specialist’s ability in a particular field to solve a problem. This program identifies the logical patterns on which an expert makes decisions and makes decisions like humans.

The main difference between expert systems and programs that simulate intelligence is that they use inference-based reasoning.

Conventional applications use fixed algorithms and methods to solve problems, while an expert system uses trial-and-error intuitive methods to solve complex problems to obtain satisfactory results.

In general, we must say that expert systems have an effective role in knowledge management and improving strategic organizational decisions.

While having great performance in processing large volumes of complex information, these systems can make decisions like an expert. Moreover, given that their decisions are based on knowledge management, they undeniably place macro business decisions.