MYCIN rules

Each if statement includes a parameter considered in a certain context that is tested for a value based on the operation. The then statement has the same syntax but provides a certainty factor. This construction basically makes no difference between the structure of the question and the answer and allows for the application of other rules as a result of a rule — although at the cost of losing modularity and clarity of the rule base. An example for a MYCIN rule is as follows ... 

The difficulty is illustrated by the pioneering study in medicine is the MYCIN Stanford project (1972-1982), but first let s look at the character of MYCIN and some of its more technical plusses and minuses. Incidentally, E-MYCIN (empty MYCIN) is the latter system for general rule addition for any domain. MYCIN rules were of the following types ... 

Rules. Rules, first pioneered by early appHcations such as Mycin and Rl, are probably the most common form of representation used in knowledge-based systems. The basic idea of rule-based representation is simple. Pieces of knowledge are represented as IE—THEN rules. IE—THEN rules are essentially association pairs, specifying that IE certain preconditions are met, THEN certain fact(s) can be concluded. The preconditions are referred to as the left-hand side (LHS) of the rule, while the conclusions are referred to as the right-hand side (RHS). In simple rule-based systems, both the... 

Buchanan,B.A., Shortliffe, E.H. "Rule Based Expert Programs The MYCIN Experiments of the Stanford Heuristic... 

The 0.7 is the certainty that the conclusion will be true given the evidence. MYCIN uses certainty factors to rank the rules or outcomes it will abandon a search once the certainty factor is less than 0.2. If the evidence is uncertain the certainties of the bits of evidence are combined with the certainty of the rule to give the certainty of the conclusion. 

Besides rules, MYCIN asks several general questions, like name or weight of a patient, and tries to find out whether or not the patient has a serious infection. Once these questions have been answered, the system focuses on particular blood disorders and validates each statement in backward-chaining mode. A part of a typical dialogue with MYCIN based on the rules would look as follows ... 

These rules are used to reason backward that is, MYCIN starts with a hypothesis that needs to be validated and then works backward, searching the rules in the rule base that match the hypothesis. The hypothesis can be either verified with a certainty factor or can be proven wrong. 

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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