What is Expert system in Artificial Intelligence?

Many problems that would typically need a human expert to solve can be solved by the AI expert system. It is founded on information learned from an authority. Expert systems and artificial intelligence are both capable of expressing and debating certain areas of expertise. The forerunner of modern artificial intelligence, deep learning, and machine learning systems were expert systems. Boost Your Skills by Learning: Digital Marketing
 
Table of Content:
1) An expert system is what?
2) Expert Systems in Artificial Intelligence: Benefits
3) Expert System Classes:
4) What elements make up an expert system?
5) The traits of an expert system are:
6) Building an Expert System: A Process

An expert system is what?
A computer software known as an expert system uses artificial intelligence (AI) techniques to mimic the decision-making and actions of a person or group of people who have knowledge and experience in a certain subject. In the 1970s, computer scientist Edward Feigenbaum, a professor of computer science at Stanford University and the founder of Stanford's Knowledge Systems Laboratory, developed the concept of expert systems.

Source: Safalta

Throughout a 1988 manuscript, Feigenbaum claimed that "knowledge processing" was taking over from "data processing" throughout the globe. The new chip technology and computer architectures meant that computers had the ability to do more than just simple calculations and were able to solve complicated issues, he said.

• Backward chaining reads and analyzes a series of data to arrive at a reason why something occurred. In order to make a medical diagnosis, one example of backward chaining would be to look at a group of symptoms.
• A strong knowledge base is essential for an expert system. Nonexperts utilize the system to solve complicated issues that would typically need a human expert, while experts provide knowledge to the knowledge base.
• Expert systems are outfitted with all the knowledge they require to tackle an issue thanks to knowledge engineers. To achieve this, they employ a variety of knowledge representation approaches, including symbolic patterns. By growing the system's knowledge base or developing new rules, its capabilities can be improved.
• By reading and analyzing a set of data, forward chaining can arrive at a reasonable conclusion about what will happen next. Making predictions about the direction of the market for shares is an example of forward chaining.

 
Expert Systems in Artificial Intelligence: Benefits

• Accessibility: Expert systems are widely accessible since software is produced in large quantities. Less Production Cost, since an expert system has a fair production cost. As a result, it lowers their price.
• Speed: Expert systems operate very quickly. Reduce the amount of effort each person makes as well.
• Less Error Rate: In compared to human mistakes, the expert system's error rate is often minimal.
• Lessened risk: Any hazardous setting that humans are unable to operate in can be used for them.
• Stablity: The knowledge will be retained for a very long time.
• Various specialties: It may be created using the expertise of several professionals.

 Expert System Classes:

• The consultant:  A consultant is a subject-matter expert with extensive knowledge in the field. He advises and instructs the executives on how to set up the expert system.
• Assistant: These individuals are less knowledgeable than those who carry out ordinary tasks, leaving the executives to handle the most important choices. This sort of expert system is the simplest and least expensive to construct, and it is crucial for business.
• The expert interface or intelligent front end: This consultant helps novice users use the existing expert system since they are experts at operating a difficult computer system or operation. 

What elements make up an expert system?

• The inference apparatus: To address a user's issue, this component of the system retrieves pertinent information from the knowledge base. It is a rules-based system that analyzes inputs and maps known information from the knowledge base to a set of rules before making judgments. A module that explains how the inference engine arrived at its result is frequently included in inference engines.
• The information base: This is where the data that the expert system uses is kept. Facts contributed by human specialists regarding the expert system's specific topic or subject area are grouped in the knowledge base. The system can obtain knowledge from outside sources and store it in the knowledge base thanks to a knowledge acquisition module that is frequently included in the knowledge base.
• The interface for users: Users engage with this area of the expert system to find a solution to their query or issue.

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The traits of an expert system are:

• It facilitates the distribution of human skills.
• One expert system may incorporate information from several human experts, which would increase the effectiveness of the answers.
• An expert system is permanent, but human experts are transient.
• Instead of using standard procedural code, expert systems may handle difficult issues by inferring new facts from known facts of knowledge, which are often expressed as if-then rules.
• One of the first genuinely effective types of artificial intelligence (AI) software was expert systems.

Building an Expert System: A Process

• Together, a domain expert and a knowledge engineer describe the issue.
• Identifying the problem's features
• The knowledge engineer converts the information into a language that computers can comprehend. He creates a thinking mechanism called an inference engine that, when necessary, may draw on knowledge.
• The Knowledge Expert also decides what kind of explanation would be helpful and how to incorporate the use of uncertain knowledge in the reasoning process.

The AI expert system can tackle many issues that would normally require a human expert to solve. It is based on information obtained from a reliable source. Expert systems and artificial intelligence may both express and debate certain areas of knowledge. Expert systems were the forerunners of current artificial intelligence, deep learning, and machine learning systems.

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