What is an Expert System?

An expert system is a computer program that uses artificial intelligence (AI) technology to mimic the judgment and behavior of a human or organization with skill and experience in a particular sector.

It accomplishes this by pulling knowledge from its knowledge base using reasoning and inference rules based on user queries. Usually, the purpose of expert systems is to support human experts rather than to replace them.

What are Knowledge Engineers?

Knowledge Engineering is the process of developing an Expert System, and its practitioners are known as Knowledge Engineers. A knowledge engineer’s primary responsibility is to ensure that the computer has all of the knowledge necessary to solve a problem. The knowledge engineer must select one or more forms for representing the needed knowledge as a symbolic pattern in the computer’s memory.

Foundation of Expert System

In the 1970s, computer scientist Edward Feigenbaum, a computer science professor at Stanford University and the creator of Stanford’s Knowledge Systems Laboratory, invented the notion of expert systems. It handles the most complicated problems as an expert by pulling knowledge from its knowledge base. The system, like a human expert, assists in decision-making for difficult issues by using both facts and heuristics. It is so named because it possesses expert knowledge in a given topic and can handle any complicated problem in that domain. These systems are intended for a specialized domain, such as medicine, science, or engineering.

Block Diagram of Expert System

An expert system’s performance is determined by the expert’s knowledge contained in its knowledge base. The more knowledge stored in the KB, the better the system’s performance. One notable example of an ES is a recommendation for spelling problems when entering the Google search box.

The following is a block diagram illustrating how an expert system works:

Block Diagram of Expert System in AI
Block Diagram of Expert System in AI

Examples of Expert Systems

There are many examples of an expert system. Some of them are given below –

  • MYCIN – One of the earliest expert systems based on backward chaining. It can identify various bacteria that can cause severe infections and can also recommend drugs based on the person’s weight.
  • DENDRAL – It was an artificial intelligence-based expert system used for chemical analysis. It used a substance’s spectrographic data to predict its molecular structure.
  • R1/XCON – It could select specific software to generate a computer system wished by the user.
  • PXDES – It could easily determine the type and the degree of lung cancer in a patient based on the data.
  • CaDet – It is a clinical support system that could identify cancer in its early stages in patients.
  • DXplain – It was also a clinical support system that could suggest a variety of diseases based on the findings of the doctor.

Capabilities of Expert Systems

The expert systems are capable of several actions, including:

  • Advising
  • Assistance in human decision making
  • Demonstrations and instructions 
  • Deriving solutions
  • Diagnosis
  • Interpreting inputs and providing relevant outputs
  • Predicting results
  • Justification of conclusions
  • Suggestions for alternative solutions to a problem

Characteristics of Expert System

The following are the important Characteristics of Expert Systems in AI:

  • The Highest Level of Expertise: In AI, the Expert system provides the greatest level of proficiency. It improves efficiency, accuracy, and problem-solving creativity.
  • Right-on-Time Reaction: An Artificial Intelligence Expert System communicates with the user in a very acceptable amount of time. The entire time must be shorter than that of an expert to obtain the best precise answer to the identical problem.
  • Good Reliability: In artificial intelligence, the expert system must be dependable and error-free.
  • Flexible: It must maintain its flexibility because it is controlled by an Expert system.
  • Effective Mechanism: In Artificial Intelligence, an expert system must have an effective process for administering the compilation of existing knowledge.
  • Capable of handling challenging decisions & problems: An expert system is capable of dealing with difficult decision issues and providing solutions.

Components of Expert System

Expert systems include five components:

  1. Knowledge Base
  2. Inference Engine
  3. Knowledge acquisition and learning module
  4. User Interface
  5. Explanation module
Architecture of an Expert System
Architecture of an Expert System
  • Knowledge Base – The knowledge base is made up of facts and rules. It comprises domain knowledge as well as problem-solving principles, methods, and intrinsic facts related to the domain.
  • Inference Engine –  The inference engine’s role is to get relevant knowledge from the knowledge base, interpret it, and find a solution to the user’s problem. The inference engine uses the rules from its knowledge base to infer new facts from known facts. Inference engines can also provide explanations and debugging tools.
  • Knowledge Acquisition and Learning Module – This component’s role is to enable the expert system to gather more and more knowledge from diverse sources and store it in the knowledge base.
  • User Interface – This module allows a non-expert user to interact with the expert system and discover a solution to a problem.
  • Explanation Module – This module assists the expert system in explaining to the user how the expert system arrived at a certain decision.

Other Key terms used in Expert Systems

Facts and Rules

A fact is a little piece of vital information. Facts on their own have extremely limited use. The rules are necessary for selecting and applying facts to a user problem.

Knowledge Acquisition

The phrase knowledge acquisition refers to how an expert system obtains needed domain knowledge. The entire process begins with the extraction of information from a human expert, followed by the conversion of the acquired knowledge into rules and the injection of the produced rules into the knowledge bas

Knowledge Extraction Process
Knowledge Extraction Process

Strategies Used By The Inference Engine

In general, the Inference Engine acquires information from the Knowledge Base using one of two strategies, namely –

  • Forward Chaining
  • Backward Chaining

Forward Chaining

An expert system can use this method to answer the question, “What can happen next?

The expert system deduces the outcome after examining all facts and rules by following a sequence of conditions and derivations. It then sorts them before reaching a decision on a viable solution.

When working on the conclusion, consequence, or impact, this method is used. For example, anticipating how the stock market will respond to changes in interest rates.

Backward Chaining

To answer the question, “Why did this happen?” an expert system employs backward chaining.

The inference engine attempts to find the conditions that may have occurred in the past to cause the final result based on what has previously happened. This approach is used to determine the cause of an event. For example, a human cancer diagnosis.

Limitations of Expert System

  • The response of the expert system may get wrong if the knowledge base contains the wrong information.
  • Like a human being, it cannot produce a creative output for different scenarios.
  • Its maintenance and development costs are very high.
  • Knowledge acquisition for designing is much more difficult.
  • For each domain, we require a specific ES, which is one of the big limitations.
  • It cannot learn from itself and hence requires manual updates.

Advantages of Expert System

The key advantages/benefits of Expert Systems in Artificial Intelligence (AI) are as follows:

  • It improves decision quality
  • Cuts the expense of consulting experts for problem-solving
  • It provides fast and efficient solutions to problems in a narrow area of specialization.
  • It can gather scarce expertise and use it efficiently.
  • Offers consistent answers for the repetitive problem
  • Maintains a significant level of information
  • Helps you to get fast and accurate answers
  • A proper explanation of decision making
  • Ability to solve complex and challenging issues
  • Artificial Intelligence Expert Systems can steadily work without getting emotional, tense, or fatigued.

Disadvantages of Expert System

The following are the drawbacks of Expert Systems in AI:

  • The expert system has no emotions.
  • Common sense is the main issue of the expert system.
  • It is developed for a specific domain.
  • It needs to be updated manually. It does not learn itself.
  • Not capable to explain the logic behind the decision.

Applications of Expert System

Some common Expert System Applications:

  • Information management
  • Hospitals and medical facilities
  • Help desks management
  • Employee performance evaluation
  • Loan Analysis
  • Virus detection
  • Useful for repair and maintenance projects
  • Warehouse optimization
  • Planning and scheduling
  • The configuration of manufactured objects
  • Financial decision-making Knowledge publishing
  • Process monitoring and control
  • Supervise the operation of the plant and controller
  • Stock market trading
  • Airline scheduling & cargo schedules

Human expert vs. Expert System

Human ExpertExpert System
Perishable and unpredictable in naturePermanent and consistent in nature
Difficult to transfer and document dataEasy to transfer and document data
Easy to transfer and document dataExpert Systems are cost-effective Systems
Difference Between Human Expert and Expert System

Conventional System vs. Expert System

Conventional SystemExpert System
Knowledge and processing are combined in one unit.The Knowledge database and the processing mechanism are two separate components.
The program does not make errors (Unless error in programming).The Expert System may make a mistake.
The system is operational only when fully developed.The expert system is optimized on an ongoing basis and can be launched with a small number of rules.
Step-by-step execution according to fixed algorithms is required.Execution is done logically & heuristically.
It needs full information.It can be functional with sufficient or insufficient information.
Difference between Conventional System and Expert System