Expert systems: MYCIN rules

Buchauau, B.G. aud Shortliffe, E.H., Eds., Rule-Based Expert Systems The MYCIN Experiments of the Stanford Heuristic Programming Project, Addisou-Wesley, Read-iug, MA, 1984. Out of priut electrouically available at http //www.aaaipress.org/Clas-sic/Buchauau/buchauau.html. 

Tools for rule-based expert systems (as well as manual methods) should evaluate the consistency and completeness of the rules. The TEIRESIAS program (Davis 1976) linked to the MYCIN infectious disease system was one of the first attempts to develop an automated verification tool. Later work by Suwa et al. (1982) for the ONCOCIN (clinical oncology) system examined a rule set as it was read into the system. This rule checker assumes that for each combination of attribute values appearing in the antecedent a corresponding rule exists. 

Whereas expert system languages for reasoning like PROLOG are universally qualified (all/all not) and nonquantitative, MYCIN allows an aspect of existential qualification (some, some not) and is quantified. The rules have both a rule form as above, and a certainty factor (CF). For those of a mathematical bent, the MYCIN certainty factor is defined by the following equation ... 

In the 1960 s, Edward Feigenbaum and other scientists at Stanford University built two early expert systems DENDRAL, which classified chemicals, and MYCIN, which identified diseases. These early expert systems were cumbersome to modify because they had hard-coded rules. By 1970, the OPS expert system shell, with variable rule sets, had been released by Digital Equipment Corporation as the first commercial expert system shell. In addition to expert systems, neural networks became an important area of artificial intelligence in the 1970 s and 1980 s. Frank Rosenblatt introduced the Perceptron in 1957, but it was Perceptrons An Introduction to Computational Geometry (1969), by Minsky and Seymour Papert, and the two-volume Parallel Distributed Processing Explorations in the Microstructure of Cognition (1986),... 

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