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. It is designed to solve complex problems by reasoning through bodies of knowledge, represented mainly as if-then rules rather than through conventional procedural code. Expert systems were among the first truly successful forms of AI software and were the first commercial systems to use a knowledge-based architecture.

An expert system is divided into two subsystems: the inference engine and the knowledge base. The knowledge base represents facts and rules, and the inference engine maps known information from the knowledge base to a set of rules and makes decisions based on those inputs. The process of building and maintaining an expert system is called knowledge engineering, and knowledge engineers ensure that expert systems have all the necessary information to solve a problem.

The components of an expert system include a knowledge base, an inference engine, an explanation facility, a knowledge acquisition facility, and a user interface. The knowledge base is where the information is stored, and it is created from information provided by human experts. The inference engine acts like a search engine, examining the knowledge base for information that matches the users query/search. The user interface is the part of the system that allows the user to interact with the expert system.

Expert systems have several advantages, including increased availability and reliability, multiple expertise, explanation, and fast response. They are widely used in many areas such as medical diagnosis, accounting, coding, games, and law. However, it is important to remember that an expert system is not used to replace human experts; instead, it is used to assist humans in making complex decisions.