 In artificial intelligence, an expert system is a computer system that emulates the decision-making ability of a human expert. The expert systems are designed to solve complex problems by reasoning through bodies of knowledge, represented mainly as it then rules rather than through conventional procedural code. The first expert systems were created in the 1970s and then proliferated in the 1980s. The expert systems were among the first truly successful forms of artificial intelligence AI software. An expert system is divided into subsystems, the inference engine and the knowledge base. The knowledge base represents facts and rules. The inference engine applies the rules to the known facts to deduce new facts. Inference engines can also include explanation and debugging abilities. The expert systems were introduced by the Stanford Heuristic Programming Project led by Edward Fegant-Bohn, who is sometimes termed the father of expert systems other key early contributors were Brutes Buchanan and Randall Davis. The Stanford researchers tried to identify domains where expertise was highly valued and complex, such as diagnosing infectious diseases, mice and identifying unknown organic molecules dental. The idea that intelligence systems would arrive their power from the knowledge they possess rather than from the specific formalisms and inference schemes they used in, as Fegant-Bohn said, was at the time the significant step forward, since the past research had been focused on heuristic computational methods, culminating in attempts to develop very general purpose problem solvers for mostly the conjunct work of Alan Newell and Herbert Simon. The expert systems became some of the first truly successful forms of artificial intelligence AI software.