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expert system

By Ben Lutkevich

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.

Expert systems are usually intended to complement, not replace, human experts.

The concept of expert systems was developed in the 1970s by computer scientist Edward Feigenbaum, a computer science professor at Stanford University and founder of Stanford's Knowledge Systems Laboratory. The world was moving from data processing to "knowledge processing," Feigenbaum said in a 1988 manuscript. That meant computers had the potential to do more than basic calculations and were capable of solving complex problems thanks to new processor technology and computer architectures, he explained.

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.

Expert systems accumulate experience and facts in a knowledge base and integrate them with an inference or rules engine -- a set of rules for applying the knowledge base to situations provided to the program.

The inference engine uses one of two methods for acquiring information from the knowledge base:

  1. Forward chaining reads and processes a set of facts to make a logical prediction about what will happen next. An example of forward chaining would be making predictions about the movement of the stock market.
  2. Backward chaining reads and processes a set of facts to reach a logical conclusion about why something happened. An example of backward chaining would be examining a set of symptoms to reach a medical diagnosis.

An expert system relies on having a good knowledge base. Experts add information to the knowledge base, and nonexperts use the system to solve complex problems that would usually require a human expert.

The process of building and maintaining an expert system is called knowledge engineering. Knowledge engineers ensure that expert systems have all the necessary information to solve a problem. They use various knowledge representation methodologies, such as symbolic patterns, to do this. The system's capabilities can be enhanced by expanding the knowledge base or creating new sets of rules.

What are the components of an expert system?

There are three main components of an expert system:

Applications and use cases of expert systems

Expert systems can be effective in specific domains or subject areas where experts are required to make diagnoses, judgments or predictions.

These systems have played a large role in many industries, including the following:

What are some examples of expert systems?

Expert systems that are in use include the following examples:

What are the advantages of expert systems?

Expert systems have several benefits over the use of human experts:

What are the challenges of expert systems?

Among expert systems' shortcomings are the following:

18 Apr 2023

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