Research on expert systems was also active in France. Expert systems became some of the first truly successful forms of artificial intelligence (AI) software. The idea that "intelligent systems derive their power from the knowledge they possess rather than from the specific formalisms and inference schemes they use" – as Feigenbaum said – was at the time a significant step forward, since the past research had been focused on heuristic computational methods, culminating in attempts to develop very general-purpose problem solvers (foremostly the conjunct work of Allen Newell and Herbert Simon). The Stanford researchers tried to identify domains where expertise was highly valued and complex, such as diagnosing infectious diseases ( Mycin) and identifying unknown organic molecules ( Dendral). Įxpert systems were formally introduced around 1965 by the Stanford Heuristic Programming Project led by Edward Feigenbaum, who is sometimes termed the "father of expert systems" other key early contributors were Bruce Buchanan and Randall Davis. These expert systems in medicine were the MYCIN expert system, the INTERNIST-I expert system and later, in the middle of the 1980s, the CADUCEUS.
This previous situation gradually led to the development of expert systems, which used knowledge-based approaches. Formal introduction & later developments Statistical pattern-matching, or probability theory. However, researchers realized that there were significant limitations when using traditional methods such as flow-charts These systems were often described as the early forms of expert systems. These early diagnostic systems used patients’ symptoms and laboratory test results as inputs to generate a diagnostic outcome. For example, biomedical researchers started creating computer-aided systems for diagnostic applications in medicine and biology. Thus, in the late 1950s, right after the information age had fully arrived, researchers started experimenting with the prospect of using computer technology to emulate human decision-making. The medical / healthcare field presented the tantalizing challenge to enable these machines to make medical diagnostic decisions. In particular, making these machines capable of making important decisions the way humans do. One of the first challenges was to make such machine capable of “thinking” like humans. Soon after the dawn of modern computers in the late 1940s – early 1950s, researchers started realizing the immense potential these machines had for modern society. 1.3 Current approaches to expert systems.
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