Rule-based reasoning

DENDRAL proved to be fundamentally important in demonstrating how rule-based reasoning could be developed into powerful knowledge-engineering tools [93]. 

One of the simplest and most powerful ways to manipulate information in the knowledge base is through rule-based reasoning, which relies on production rules. A production rule links two or more items of information in a structure of the form ... 

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. 

Finally, the knowledge of the current values of discrete switch states can be used to detect and to isolate switch faults by rule-based reasoning. If switches toggle then-state at a high frequency little time is left for mode identification. However, again, parallel processing can help to cope with the time constraints. 

Case-based reasoning. The main advantage of CBR systems for NDT data interpretation is that they can cope with data coming from inspection of varying constructions under varying conditions with various system settings due to their ability to learn from the data classified by the operator. In such situations no reliahle statistical classifier can be designed, and the rule-hased classifiers would be either very inefficient or unpractically complex. 

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. 

Similar to attributes, procedures can be inherited by child frames when inheritance is supported. Object-oriented programming enormously enlarges the flexibility of frame-based systems. A disadvantage is, however, that it results often in unclear and intractable reasoning structures. Table 43.2 shows the small rule base of Fig. 43.2, translated in an object-oriented system. 

From the different planning methods available within SNP, SNP optimization is selected because it offers the best fit to the customer requirements outlined above. The main reasons for this decision are the multisourcing characteristics of the supply network as well as the fact that the objective functions used by the SNP optimizer, profit maximization or cost minimization, correspond to the planning philosophy favored by the customer. In addition to SNP optimization with its cost-based approach, SNP offers several heuristic-based planning methods which follow a rule-based logic. 

When problem solving is based on experience which is difficult to define as explicit rules, it is possible to apply case-based reasoning (CBR). CBR uses directly solutions of old problems to solve new problems. The functional steps in CBR are (Gonzalez and Dankel, 1993) ... 

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