Expert rule-based

Expert systems. In situations where the statistical classifiers cannot be used, because of the complexity or inhomogeneity of the data, rule-based expert systems can sometimes be a solution. The complex images can be more readily described by rules than represented as simple feature vectors. Rules can be devised which cope with inhomogeneous data by, for example, triggering some specialised data-processing algorithms. 

The recognition ratios achieved by CBR systems developed as part of this project could not be bettered by either neural-network classifiers or rule-based expert system classifiers. In addition, CBR systems should be mote reliable than simple classifiers as they are programmed to recognise unknown data. The knowledge acquisition necessary to build CBR systems is less expensive than for expert systems, because it is simpler to describe the knowledge how to distinguish between certain types of data than to describe the whole data contents. 

The certainty factor approach has been among the more popular rule-based approaches to uncertainty. However, although it is easy to apply given the individual CFs, acquiring the raw CFs from the experts is often quite difficult. Further, although the formulas for CF combination are mathematically appealing, they often have no relation to the ways in which experts combine evidence to arrive at conclusions. Some of the task-specific approaches discussed later address uncertainty combination in a more intuitive way (35). 

Foulkes et al. (1988) have approached the synthesis of operating procedures from a more empirical angle. They have extended the work of Rivas and Rudd (1974) for the synthesis of complex pump and valve sequencing operations, relying on the use of logical propositions (implemented as rule-based expert systems), which capture the various types of constraints imposed on the states of a processing system. 

The most popular representation scheme in expert systems is the mle-based scheme. In a rule-based system the knowledge consists of a number of variables, also called attributes, to which a number of possible values are assigned. The rules are the functions that relate the different attributes with each other. A mle base consists of a number of If... Then... mles. The IF part contains the conditions that must be satisfied for the actions or conclusions in the THEN part to be valid. As an example, suppose we want to express in a mle that a compound is unstable in an alkaline solution if it contains an ester function. In a semi-formal way the mle can be written ... 

Rules seemingly have the same format as IF.. THEN.. statements in any other conventional computer language. The major difference is that the latter statements are constructed to be executed sequentially and always in the same order, whereas expert system rules are meant as little independent pieces of knowledge. It is the task of the inference engine to recognize the applicable rules. This may be different in different situations. There is no preset order in which the rules must be executed. Clarity of the rule base is an essential characteristic because it must be possible to control and follow the system on reasoning errors. The structuring of rules into rule sets favours comprehensibility and allows a more efficient consultation of the system. Because of the natural resemblance to real expertise, rule-based expert systems are the most popular. Many of the earlier developed systems are pure rule-based systems. 

In these frames all specific columns that are relevant for the reasoning process of the expert system can be described in a structured and comprehensive way. The frame-based and rule-based knowledge representation are both required to represent expertise in a natural way. Therefore, in most expert systems a combination of rule-based and frame-based knowledge representation is used. The rule base together with the factual and descriptive knowledge by means, of e.g., frames constitute the knowledge base of the expert system. 

An example will clarify this. Suppose we have a small expert system that can give advice on a suitable solvent and the rule base consists of the following six rules, here represented in a semi-formal way ... 

Widely used in rule-based systems, input-output matching approaches are used in conventional expert systems going back to the earliest systems. 

The information to which the rule is applied might be extracted from the knowledge base, it might be provided by the user in response to questions from the ES, or it may be provided by combining the two. An expert system that uses rule-based reasoning is, quite reasonably, known as a rule-based system. This is the most widely used form of expert system in science, and it is on this type of system that this chapter concentrates. 

Ignizio, J.P, An Introduction to Expert Systems The Development and Implementation of Rule-Based Expert Systems, McGraw Hill, New York, 1991. 

Thus, propanol, C3H70H, has a membership of 1 in the three-carbon molecule class, while ethanol, C2H5OH, has a membership of 0 in the same class. As the membership in a crisp set must take one of only two possible values, Boolean (two-valued) logic can be used to manipulate crisp sets. If all the knowledge that we have can be described by placing objects in sets that are separated by crisp divisions, the sort of rule-based approach to the development of an expert system described in the previous chapter is appropriate. 

In a conventional expert system, the only rules to fire are those for which the condition is met. In a fuzzy system, all of the rules fire because all are expressed in terms of membership, not the Boolean values of true and false. Some rules may involve membership values only of zero, so have no effect, but they must still be inspected. Implicitly, we assume an or between every pair of rules, so the whole rule base is... 

OncoLogic Rule-based expert system for the prediction of carcinogenicity... 

The contents of a knowledge base, the facts and rules, or heuristics about a problem will be discussed shortly. The problem-solving and inference engine is the component of the system that allows rules and logic to be applied to facts in the knowledge base. For example, in rule-based expert systems, "IF-THEN" rules (production rules) in a knowledge base may be analyzed in two ways ... 

Diagnosis is accomplished by the expert system. The central part of the expert system is the rule base. The rule base consists of ideas, called nodes, and rules which interconnect them as shown in Figure 2. The upper node is the evidence the lower node is the conclusion. The rule between them will state that if the evidence is known to be true with absolute certainty, then the conclusion will be known to be true (or false) with a specific confidence. 

Figure 2. Basic Step in an Expert System Rule Base.

Buchanan, B. G. Shortliffe, E. H. "Rule Based Expert-Systems" Addison-Wesley, 1984, Chap. 10. 

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