Imagine you’re in a situation where you need expert advice, but the expert is not available. Maybe you want to plant a garden but don’t know which plants are best for your climate or soil, or perhaps you need help with diagnosing a problem in a machine. Normally, you’d ask an expert who has years of experience in that field. But what if you could have access to an expert’s knowledge anytime you needed it, without calling someone? That’s where expert systems come in.
What is an Expert System?
An expert system is a type of computer program that helps solve problems by using the knowledge and decision-making skills of an expert in a particular field. Instead of relying on a human expert, you rely on a computer program that has been trained with expert knowledge. This program can answer questions, solve problems, and make decisions the way an expert would.
Think of it like asking a computer to “think” and provide solutions based on its expert knowledge. It’s like having a virtual expert available whenever you need help!
How Does an Expert System Work?
An expert system is built with three main parts:
- The Knowledge Base
The knowledge base is the heart of the expert system. It’s a collection of information, facts, and rules that an expert would use to make decisions. Experts help create this knowledge base by writing down rules like “If the temperature is above 90°F, then water the plant more often,” or “If the patient has a fever and a cough, then they might have a cold.” This part of the system stores all the knowledge, just like a big library of information. - The Working Memory
This is where the expert system keeps track of the current situation or facts it’s working with. For example, in the gardening example, the system might store details like “The garden has a lot of sun,” or “The soil is clay-based.” In a medical system, it might store things like “The patient has a fever and a sore throat.”The working memory keeps track of everything that’s important for solving the current problem. - The Inference Engine
The inference engine is the part of the system that actually does the thinking. It looks at the facts in the working memory and compares them to the rules in the knowledge base to figure out what’s happening and what should be done next. For example, if the rule says, “If the soil is dry, then water the plant,” and the system knows that the soil is dry, the inference engine will give the recommendation, “Water the plant.” This part helps the system make decisions and solve problems, just like an expert would do.
How Expert Systems Are Built
Building an expert system is like creating a recipe for solving problems. The first step is gathering a lot of information about the area you want the system to help with. Experts in that field work with computer scientists to turn their knowledge into a set of rules.
Once the rules are written and stored in the knowledge base, the system can be tested. During testing, it’s used to solve real problems and see if it gives the right answers. Just like when you learn from your mistakes, an expert system improves over time. If it makes an error, the system’s rules can be updated to make it better.
Real-World Examples of Expert Systems
Let’s take a look at some real-world examples of expert systems that have been developed:
- MYCIN
MYCIN was one of the first expert systems, created in the 1970s to help doctors diagnose bacterial infections. It asked questions about the patient’s symptoms and test results, and then it used rules to give the doctor a recommendation about what the infection might be and what treatment could work. Although MYCIN is no longer in use today, it helped pave the way for the development of medical expert systems. - XCON
In the 1980s, a company called Digital Equipment Corporation (DEC) created an expert system called XCON. DEC needed help with managing the complex parts used in assembling computers. XCON would check that each order had the right components before the computer was assembled, reducing errors and saving time. By using the system, DEC could send out more computers without worrying about mistakes in the orders. - DELTA
General Electric (GE) created another expert system called DELTA in the 1980s. This system was used to troubleshoot and repair diesel engines. It captured the knowledge of David I. Smith, a senior GE engineer who was known as an expert in locomotive engines. DELTA helped other engineers and technicians fix problems by using over 500 rules about how to diagnose and repair different types of engine issues.
Why Are Expert Systems Useful?
Expert systems have many benefits:
- Efficiency: They allow companies and individuals to solve problems quickly without needing an expert present. This can save time and money.
- Access to Expertise: Since expert systems can be available 24/7, anyone with access to the system can get expert advice, even if the expert is unavailable or retired.
- Consistency: Expert systems always make decisions based on the same rules, ensuring consistency in problem-solving.
How Do Expert Systems Keep Improving?
Creating an expert system is a process that doesn’t stop once the program is running. As new information and situations arise, the system needs to be updated. Experts regularly add new rules or tweak the existing ones to ensure that the system stays up to date. Over time, the system gets better at solving problems, just like an expert who continues learning and gaining experience.
Conclusion
Expert systems are a powerful tool that allow us to store and use the knowledge of experts, making it possible to get expert advice and solve problems without needing an actual expert around. They have been used in many industries, from healthcare to manufacturing, and continue to improve over time.
By mimicking the way experts think, expert systems help people make decisions and solve problems more efficiently. They may not replace human experts, but they’re a valuable tool for using their knowledge to help others.
