Expert systems are a branch of artificial intelligence that leverage databases of knowledge and inference rules to simulate the expertise and decision-making skills of human specialists in particular fields. These systems are primarily composed of a knowledge base containing domain-specific facts and heuristics, and an inference engine that applies logical rules to the knowledge base to deduce new information or make decisions. They are designed to perform complex problem-solving and decision-making tasks, often in areas where human expertise is scarce, expensive, or difficult to access. Expert systems have been applied across various domains, including medicine for diagnostic purposes, finance for investment analysis, and engineering for design and troubleshooting.
Historical overview: The concept of expert systems gained prominence in the 1970s and 1980s as a major application of artificial intelligence, with the development of systems like MYCIN in the mid-1970s, which was designed to diagnose bacterial infections and recommend antibiotics.
Key contributors: Among the key figures in the development of expert systems were Edward Feigenbaum, often referred to as the "father of expert systems," who made significant contributions to their theoretical foundations and practical applications, particularly with the development of the DENDRAL and MYCIN systems.