Artificial IntelligenceBlogEducationExpert System

Expert System in Artificial Intelligence

What is an expert system?

An expert system is a computer program that uses artificial intelligence (AI) technologies to simulate the judgment and behavior of a human or an organization that has expertise and experience in a particular field.

How does an expert system work?

Modern expert knowledge systems use machine learning and artificial intelligence to simulate the behavior or judgment of domain experts. These systems can improve their performance over time as they gain more experience, just as humans do.

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 or Rules 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 Base: 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.

The Inference Engine generally uses two strategies for acquiring knowledge from the Knowledge Base, namely – 

  1. Forward chaining – Answers the question, “What can happen in the future?”
  2. Backward chaining – Answers the question, “Why did this happen?”
  • Forward Chaining
  • Backward Chaining

Forward Chaining – 
Forward Chaining is a strategic process used by the Expert System to answer the questions – What will happen next. This strategy is mostly used for managing tasks like creating a conclusion, result or effect. Example – prediction or share market movement status.

Backward Chaining – 
Backward Chaining is a strategy used by the Expert System to answer the questions – Why this has happened. This strategy is mostly used to find out the root cause or reason behind it, considering what has already happened. Example – diagnosis of stomach pain, blood cancer or dengue, etc.  

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.

Advantages 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.

Disadvantages of Expert System

  • 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

The application of an expert system can be found in almost all areas of business or government. They include areas such as –

  • Different types of medical diagnosis like internal medicine, blood diseases and show on.
  • Diagnosis of the complex electronic and electromechanical system.
  • Diagnosis of a software development project.
  • Planning experiment in biology, chemistry and molecular genetics.
  • Forecasting crop damage.
  • Diagnosis of the diesel-electric locomotive system.
  • Identification of chemical compound structure.
  • Scheduling of customer order, computer resources and various manufacturing task.
  • Assessment of geologic structure from dip meter logs.
  • Assessment of space structure through satellite and robot.
  • The design of VLSI system.
  • Teaching students specialize task.
  • Assessment of log including civil case evaluation, product liability etc.

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