Artificial intelligence, when properly integrated, can help your firm prepare for new revenue sources. It has proven to be effective in a variety of fields, including eCommerce, healthcare, fraud detection, and data security. As businesses have become more familiar with the terms artificial intelligence, machine learning, and big data, it is even more critical to now understand the key components and concepts of artificial intelligence and get ready for the implementation. One such concept is Expert Systems. Let’s discuss the expert systems in AI and get a better understanding of them.
What is an Expert System?
An expert system is a computer system or a piece of software that simulates the decision-making abilities and judgment of an expert human or organization in artificial intelligence. This is accomplished by retrieving relevant information from its knowledge base and interpreting it in the light of the user’s problem. A knowledge base is a collection of acquired experience that has been loaded and tested into the system by an expert. Humans who are experts in a certain topic add data to the knowledge base, and this software is used by non-expert users to obtain information. Expert systems can have their knowledge augmented by adding to the knowledge base or adding to the rules. The more experience that is entered into the expert system, the better the system’s performance will be.
High performance, understandability, reliability, flexibility, and high responsiveness are the characteristics of expert systems. Expert systems, rather than using traditional procedural code, are supposed to handle complicated issues by reasoning through bodies of knowledge, which are primarily represented as if-then rules. Expert systems specialize in a single problem domain, such as medicine, science, or engineering. DENDRAL, MYCIN, R1/XCON, PXDES, and CaDet are some popular examples of expert systems.
Components of Expert Systems
- Knowledge Base: Problem-solving principles, processes, and intrinsic facts relevant to the problem domain make up the Knowledge Base. It contains all information regarding the problem domain. It’s similar to a vast container of knowledge gathered from various experts on a certain topic.
- User Interface: The expert system communicates with the user through a user interface, takes queries as input in a legible format, and delivers them to the inference engine. It displays the output to the user after receiving the response from the inference engine. In other words, it’s a user interface that allows a non-expert user to communicate with an expert system in order to solve a problem.
- Interface Engine: The inference engine is the expert system’s brain. It’s a generic control technique for arriving at a solution or conclusion by applying the knowledge base’s deductive knowledge to task-specific facts. When attempting to answer the user’s inquiry, it selects facts and rules to apply.
Expert systems are adept at reasoning, classification, configuration, pattern-matching, and diagnosis. It is frequently employed in a variety of fields, including medical diagnosis, accountancy, coding, planning, scheduling, and games. The Pathfinder system was the first expert system to be approved by the American Medical Association. This decision-theoretic expert system for hematopathology diagnosis was developed at Stanford University in the 1980s. Expert systems are also utilized in the design and manufacturing of autos and other electronic devices such as cameras. Other than these, expert systems are employed in the knowledge domain to give the required knowledge to the client. It is also being used in the banking industry to detect any probable fraud or suspicious conduct, as well as to advise bankers on offering business loans.
Advantages – Expert Systems
- Quick reaction
- Accessibility is inexpensive
- They may be programmed to work 24 hours a day, seven days a week without the need for human involvement.
- Expert Systems have a significant edge in terms of accuracy.
- If the knowledge base has correct knowledge, the chances of making an error are reduced.
- They react to a certain query at a breakneck speed.
- Expert Systems are widely available because they are simple to build and thus to replicate.
Disadvantages – Expert Systems
- The expert system’s main concern is common sense.
- The expert system is devoid of feelings.
- It must be manually updated. It does not acquire knowledge on its own.
- Any inconsistency in the data can lead to faulty decision-making.
- Its upkeep and development costs are rather high.
Expert systems, as expected, are being developed and used in a wide range of applications around the world, owing to their symbolic reasoning and explanation capabilities. They have progressed to the point where they’ve sparked arguments in the world of artificial intelligence making it an interesting trend to keep an eye on.
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