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Expert System in Artificial Intelligence: What is, Characteristics, Applications & Benefits

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4th Feb, 2021
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Expert System in Artificial Intelligence: What is, Characteristics, Applications & Benefits

What is an Expert System?

In artificial intelligence (AI), an expert system is a computer-based decision-making system. It is designed to solve complex problems. To do so, it applies knowledge and logical reasoning and adheres to certain rules. An expert system is one of the first successful forms of artificial intelligence.

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Characteristics of Expert System in Artificial Intelligence

Following are the important characteristics of an expert system in AI:

  • Highest Level of Expertise: An expert system in artificial intelligence provides the highest level of expertise along with efficiency and accuracy.
  • Reaction Time: An expert system in artificial intelligence has a very low reaction time. It takes less time than a human expert to solve the same complex problem.
  • Reliable: An expert system in artificial intelligence is reliable and error-free.
  • Flexible: An expert system in artificial intelligence is flexible to tackle different problems.
  • Effective: An expert system in artificial intelligence has a robust mechanism to resolve complex problems and later administer them. 
  • Capable: An expert system in artificial intelligence can handle complex problems and deliver solutions on time.

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Expert System in Artificial Intelligence Components

The expert system in AI has the following components:

  • User interface – It is the most important part of the expert system software. The user interface transfers the queries of the user and into the inference engine. Then it shows the results to the user. It acts as a two-way communicator between the expert system and the user.
  • Inference engine – The inference engine is the central processing unit of the expert system. An inference engine works on rules and regulations to solve complex problems. It uses information from the knowledge base. It smartly selects factual data and rules, and processes and applies them to answer the user’s query. It also gives proper reasoning about the data in the knowledge base. This helps detect and deduce complex problems and prevents recurrence.  And the last, the inference engine formulates conclusions.

The inference engine has the following strategies:

  1. Forward chaining – Answers the question, “What can happen in the future?”
  2. Backward chaining – Answers the question, “Why did this happen?”
  • Knowledge base – The knowledge base is the information center. It contains all the information about problem domains. It is like a large repository of information collected from various experts.

Knowledge Base Components

Factual and heuristic knowledge is stored in the knowledge base.

  • Factual Knowledge − Information pertaining to knowledge engineers.
  • Heuristic Knowledge − Ability to evaluate and guess.

Other Key Terms used in Expert System

Apart from the expert system components listed above, the following terms are also extensively used when discussing expert systems.

  • Facts and rules – A fact is a small piece of important knowledge. Facts have limited use. An expert system selects the rules to solve a problem.
  • Knowledge acquisition – Knowledge acquisition refers to the method used to extract domain-specific information by an expert system. The process begins by acquiring knowledge from a human expert, converting human knowledge into facts and rules, and finally feeding those rules into the knowledge base.

Participants in Expert System Development in Artificial Intelligence

Following are the key group of people who are part of the expert system

  • Domain expert – A person or group of people whose skills and knowledge are acquired to develop the knowledge base.
  • Knowledge engineer – A technical person who uses the acquired knowledge and integrates it with expert computer systems.
  • End-user – It is a person or group who uses the expert system to fetch advice not provided by a domain expert.

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Building an Expert System in Artificial Intelligence

Follow these steps to build an expert system in artificial intelligence

  1. Determine or decipher the characteristics of the problem.
  2. Knowledge engineers and domain experts collaborate to define or decipher the issue.
  3. The knowledge engineer, after defining the problem, translates it into understandable computer language knowledge. The knowledge engineer designs the inference engine that uses the knowledge when called to assist.
  4. The knowledge expert also integrates the use of unknown knowledge in the reasoning process with an explanation.

Expert System Technology in Artificial Intelligence

Expert system includes the following technologies :

  • Expert System Development Environment − It includes hardware such as workstations and minicomputers.
  • High-level Symbolic Programming Languages like PROgrammation en LOGique (PROLOG) and LISt Programming (LISP).
  • Large databases.
  • Tools − Reduces the work and is cost-efficient.
  • Shells − An expert system that does not have a knowledge base. 

Conventional System vs Expert System

The following table describes the differences between a conventional and an expert system.

Conventional SystemExpert System
Combined processing and knowledge units.Processing mechanisms and knowledge databases are separate entities.
The program seldom makes errors (only programming errors).The expert system does make mistakes.
The system operates only when ready.The expert system optimizes continuously and launches with minor rules.
Procedural execution takes place as per fixed algorithms.Execution takes place logically.
Requires full data.It is functional with full or less data.

Human Expert vs Expert System

The following table describes the differences between a human expert and artificial intelligence.

Human ExpertArtificial Intelligence
Difficult to transferTransferable
Difficult to documentEasy to document
ExpensiveA cost-effective system

Benefits of Expert System in Artificial Intelligence

Following are the benefits of an expert system: 

  • Improves decision-making quality.
  • Cost-effective, as it trims down the expense of consulting human experts when solving a problem.
  • Provides fast and robust solutions to complex problems in a specific domain.
  • It gathers scarce knowledge and uses it efficiently.
  • Offers consistency when providing answers for repetitive issues.
  • Maintains a good amount of information.
  • Provides fast and accurate answers. 
  • Provides a proper explanation for decision making.
  • Solves complex and challenging issues.
  • Works steadily without fatigue.

Limitations of Expert System in Artificial Intelligence

Following are the limitations of an expert system:

  • Not capable of making decisions in extraordinary situations.
  • Garbage-in Garbage-out (GIGO), if there is an error in the knowledge base, we are bound to get wrong decisions.
  • The maintenance cost is more.
  • Each problem is different, and expert systems have some limitations when it comes to solving varied problems. In such cases, a human expert is more creative.

Applications of Expert System in Artificial Intelligence

Following are a few of the applications of an expert system:

  • MYCIN: It identifies various bacteria that cause acute infections. Drugs are recommended on the patient’s weight. 
  • DENDRAL: It is an expert system to predict molecular structure using chemical analysis.
  • PXDES: It predicts the phase and type of lung cancer
  • CaDet: Identifies cancer in early stages
  • Information management
  • Hospitals and medical facilities
  • Help desk management
  • Employee performance evaluation
  • Loan analysis
  • Virus detection
  • Maintenance and repair of projects
  • Warehouse optimization
  • Planning and scheduling
  • The configuration of manufactured objects
  • Assist in financial decision making
  • Process monitoring and control
  • Supervise the plant operation and controller
  • Stock market trading
  • Airline scheduling and cargo schedules
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An expert system is any computer-based decision-making system that is interactive and reliable to resolve complex problems. An expert system is used for applications such as human resources, stock market, and so on. Key benefits of expert systems are better decision quality, cost reduction, consistency, speed, and reliability. An expert system does not give out of the box solutions, and the maintenance cost is high. 

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Pavan Vadapalli

Blog Author
Director of Engineering @ upGrad. Motivated to leverage technology to solve problems. Seasoned leader for startups and fast moving orgs. Working on solving problems of scale and long term technology strategy.
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Frequently Asked Questions (FAQs)

1What are the important characteristics of an expert system in AI?

Artificial intelligence expert systems give the highest level of competence, as well as efficiency and accuracy. Artificial intelligence expert systems have a very fast reaction time. It takes less time to tackle a complex problem than a human expert. Artificial intelligence expert systems are dependable and error-free. Artificial intelligence expert systems are adaptable to a variety of problems. In artificial intelligence, an expert system provides a robust mechanism for resolving complicated problems and then administering them. Artificial intelligence expert systems can handle difficult problems and provide timely solutions.

2How to build an expert system in Artificial Intelligence?

Determine or comprehend the problem's characteristics. To describe or decipher the problem, knowledge engineers & domain experts interact. After identifying the problem, the knowledge engineer converts it into comprehensible computer language knowledge. The knowledge engineer creates the inference engine, which employs the knowledge when it is needed. The knowledge expert also provides an explanation for the use of unidentified data in the reasoning process.

3What are the limitations of the expert system in Artificial Intelligence?

They are incapable of making decisions under unusual circumstances. Garbage-in, garbage-out (GIGO) means that if there is a mistake in the knowledge base, we will make bad decisions. The expense of upkeep is higher. Expert systems have several limitations when it comes to handling various problems because each problem is unique. A human expert is more innovative in these situations.

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