We are independent & ad-supported. We may earn a commission for purchases made through our links.
Advertiser Disclosure
Our website is an independent, advertising-supported platform. We provide our content free of charge to our readers, and to keep it that way, we rely on revenue generated through advertisements and affiliate partnerships. This means that when you click on certain links on our site and make a purchase, we may earn a commission. Learn more.
How We Make Money
We sustain our operations through affiliate commissions and advertising. If you click on an affiliate link and make a purchase, we may receive a commission from the merchant at no additional cost to you. We also display advertisements on our website, which help generate revenue to support our work and keep our content free for readers. Our editorial team operates independently of our advertising and affiliate partnerships to ensure that our content remains unbiased and focused on providing you with the best information and recommendations based on thorough research and honest evaluations. To remain transparent, we’ve provided a list of our current affiliate partners here.

Our Promise to you

Founded in 2002, our company has been a trusted resource for readers seeking informative and engaging content. Our dedication to quality remains unwavering—and will never change. We follow a strict editorial policy, ensuring that our content is authored by highly qualified professionals and edited by subject matter experts. This guarantees that everything we publish is objective, accurate, and trustworthy.

Over the years, we've refined our approach to cover a wide range of topics, providing readers with reliable and practical advice to enhance their knowledge and skills. That's why millions of readers turn to us each year. Join us in celebrating the joy of learning, guided by standards you can trust.

What Are Expert Systems?

By Jessica Reed
Updated: May 16, 2024

Technology has always been about building better, faster, and smarter machines. Expert systems embrace this concept by using advanced computer logic to create software that appears to "think" and make decisions on its own. Traditionally built on Boolean logic — logic using only true or false values — expert systems use complex algorithms to calculate answers from a large database of information. If the computer cannot determine the correct answer, it is assumed not that the program is wrong but that the knowledge base does not contain enough information on the subject.

When a computer must make a decision, it all breaks down to a series of true or false statements. If programmed to light up when a button is pressed, then pressing the button sets it to true and not pressing the button sets it to false. False means no light while true turns the light on. This is the basis of computer logic.

An expert system takes these true and false answers to a new level. By combining a series of true and false answers, the computer tries to determine how to react to a certain situation. It may change its response based on the specific pattern and number of true and false answers.

The idea behind these systems is based on how people think. Humans can store vast amounts of new knowledge and make decisions based on previous knowledge. The computer is programmed to “think” and make decisions based on the knowledge found in its database and on its previous experiences. In a fashion, it’s as if the computer is "learning" from its past successes and failures.

Two main forms of expert systems exist. The traditional expert system uses Boolean logic to makes its decisions. A fuzzy logic expert system, on the other hand, does not. It calculates a range of values that fall in between simple true or false answers to determine to what degree a statement is more true or more false.

Fuzzy expert systems are more human-like than traditional expert systems in the way they "think." These expert systems are not told specific answers to a problem, but rather given one statement from which they draw additional conclusions. This process is known as inference.

For example, if a statement reads "All female cats are striped. Miss Kitty is a female cat," fuzzy expert systems would infer that since all female cats are striped and Miss Kitty is a female cat, then Miss Kitty must be striped. Fuzzy logic can also calculate more complicated values, such as determining the likelihood of a specific female cat being striped if only a percentage of female cats have stripes. Traditional expert systems would need much more instruction to reach these same conclusions.

EasyTechJunkie is dedicated to providing accurate and trustworthy information. We carefully select reputable sources and employ a rigorous fact-checking process to maintain the highest standards. To learn more about our commitment to accuracy, read our editorial process.
Discussion Comments
EasyTechJunkie, in your inbox

Our latest articles, guides, and more, delivered daily.

EasyTechJunkie, in your inbox

Our latest articles, guides, and more, delivered daily.