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 and provide decision-making ability like a human expert by extracting knowledge from its knowledge base using reasoning and inference rules according to the user queries. 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 Stanfords Knowledge Systems Laboratory.
Expert systems are not intended to replace human experts but rather to complement them. They can advise users as well as provide explanations to them about how they reached a particular conclusion or advice. The performance of an expert system is based on the experts knowledge stored in its knowledge base. The more knowledge stored in the knowledge base, the more the system improves its performance.
Some characteristics of expert systems include high performance, understandable, reliable, and highly responsive. The components of an expert system include a knowledge base, an inference engine, an explanation facility, a knowledge acquisition facility, and a user interface.
Examples of expert systems that are in use include CaDet (Cancer Decision Support Tool) used to identify cancer in its earliest stages and DENDRAL, which helps chemists identify unknown organic compounds. Expert systems are widely used in many areas such as medical diagnosis, accounting, coding, games, and more.
