What Are Expert Systems In AI? Complete Guide

What is an Expert System?

Expert Systems are interactive, trustworthy computer-based decision-making tools that utilize data and heuristics to address challenging decision-making issues. It is regarded as representing the pinnacle of human knowledge and wisdom. An expert system's job is to resolve the trickiest problems in a specific field. 

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Characteristics of Expert System

Follow are the characteristics of an expert system.

• A human expert can change, but an expert system can last forever.
• It facilitates the distribution of human expertise.
• The expert system might incorporate knowledge from multiple human experts, which would increase the effectiveness of the answers.
• It lowers the expense of seeking advice from a specialist in various fields, including medical diagnosis.
• Instead of using standard procedural code, expert systems can handle complex issues by inferring new facts from known facts of knowledge, which are typically represented as if-then rules.

Components of Expert System

There are five components of the expert system in ai:

• Knowledge base: The knowledge base contains facts and regulations in an expert system. It includes norms for problem-solving and formulating methods pertinent to the domain and knowledge in specific disciplines.
• Inference engine: The inference engine's most fundamental job is to gather pertinent information from the knowledge base, analyze it, and identify a solution to the user's issue. Inference engines also possess explanatory and troubleshooting skills.
• Knowledge acquisition and learning module: With the help of this component, expert systems can gather more information from numerous sources. After that, the knowledge is stored in the knowledge base.
• User interface: With this element, a non-expert user can communicate with the expert system and develop solutions.
• Explanation module: This module gives the user a justification for the conclusion.

Examples of Expert Systems

The following are some examples of expert systems:

• MYCIN: It could recognize different bacteria that might cause acute infections and was based on backward chaining. Additionally, it might suggest medications based on the weight of the patient. It is among the top examples of an expert system.
• DENDRAL: A molecular structure prediction tool for chemical analysis.
• CaDet: It's one of the best examples of an expert system that can detect cancer in its earliest stages.
• PXDES: The kind and stage of lung cancer are identified using the PXDES expert system. It takes a photo of the upper body, which resembles the shadow, to identify the condition. This shadow determines the kind and severity.

Benefits of Expert Systems

Using expert systems instead of human experts has several advantages:

• Accuracy: Expert systems are immune to emotional or human inaccuracy. They base their choices on facts and rules.
• Permanent: When human specialists leave their positions, the technical information could follow. Knowledge-based systems offer an everlasting reservoir of information and knowledge.
• Logical deduction: Expert systems use a variety of principles, such as if-then rules, to derive conclusions from data already known.
• Cost-control: When compared to the cost of hiring human specialists, expert systems are comparatively cheap. They can assist in making judgments more quickly and inexpensively.
• Several experts: The knowledge base of an expert system is augmented by multiple experts. This gives more information to draw upon and stops one expert from influencing the decision-making process.

Expert System Applications

• It has a wide range of applications in designing and manufacturing tangible objects, including designing and producing automobiles and camera lenses.
• These systems are typically used in the knowledge domain to disseminate pertinent information to users. Tax advisors and advisors are the two standard ES used for this subject.
• It is used in the financial sector to identify any potential fraud and suspicious conduct.
• Expert system in ai is used in the evaluation and repair of equipment. The ES system is utilized in medical diagnosis and was the first application of these systems.

Conventional System vs. Expert System

The crucial difference between expert systems and conventional problem-solving software is the method used to encode the problem-related expertise. In traditional applications, data structures and programs are both used to encode problem expertise. All problem-related expertise is represented in data structures alone, not in programs, in the expert method.

Conventional systems are unable to explain a specific solution to a problem. These systems aim to provide straightforward solutions. However, expert systems can justify why certain information is required during a process and how a specific result was achieved.

Typically, an expert system executes its inference by symbolic computations using the symbols for knowledge, such as the rules, various forms of networks, frames, scripts, etc. Nevertheless, these terms cannot be expressed by traditional systems. They oversimplify the issues and cannot articulate the "how, why" questions.

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Human Expert vs. Expert System

The significant distinction between expert systems in artificial intelligence and human experts is that expert systems process knowledge represented in the form of rules and use representational reasoning in a limited area, whereas human experts use knowledge in the form of heuristics of rules of thumb to solve problems in a limited domain.

Expert systems are always accessible, unlike human experts, who are only available during certain hours of the day. Expert systems handle any problem in a concise amount of time, but human experts can take their time.

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Conclusion

In conclusion, expert systems are interactive and trustworthy at solving complicated issues. It is used in programs for human resources, medicine, and other purposes. Improved decision quality, cost savings, consistency, quickness, and dependability are some of the main advantages of expert systems in ai. 

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FAQs

1. What is an expert system in AI?

An expert system is computer software that uses artificial intelligence (AI) technologies to replicate the decision-making and actions of a person or group with knowledge and experience in a particular field.

2. What is an expert system with examples?

A computer program that simulates the judgment of a human expert is known as an expert system in ai. A few examples of an expert system are DENDRAL, a molecular structure prediction tool for chemical analysis. Another example of an expert system that predicts the kind and extent of lung cancer is PXDES.

3. What is an expert systems, and what types?

Expert systems can be classified into five categories:

• Rule-based expert systems
• Frame-based expert systems
• Neural expert systems
• Fuzzy expert systems
• Neuro-fuzzy expert systems.

4. What are the five components of an expert system?

There are five fundamental parts of an expert system: 

• Knowledge base
• An inference engine
• An explanation component
• A user interface
• An acquisition component.

5. What are the benefits of an expert system?

Expert systems can monitor production factors, compile data, and spot processes that deviate from the norm, indicating potential issues.

It can offer reliable solutions for routine decisions, procedures, and duties. It can function continually without being constrained by human constraints.

6. What are the features of an expert system?

Some features of the expert system are: 

• They perform at a high level.
• They are simple to comprehend.
• They are entirely trustworthy.
• They are pretty receptive.