Rule-based knowledge representation

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  

IF the molecule contains an ester-function THEN it is unstable in an alkaline solution 

The syntax for implementing this in an actual expert system is, of course, quite diverse and it is beyond the scope of this book to describe this in more detail. As an example the above-mentioned mle is translated in a realistic syntax. This starts by the definition of the attributes and their possible values. In this examples two attributes must be defined  

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. 

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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. 

The mixture of purely algorithmic and rule-based knowledge representation methods is an appropriate approach to follow. Where algorithmic solutions are available, they clearly win. However, the higher levels of design are difficult to quantify and thus rule-based approaches will be appropriate in the initial stages of CAD system development until more algorithmic solutions can be discovered. 

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... 

Rules are cleady a usehil form of representation for knowledge-based appHcations, with their advantages of representational simplicity, wide apphcabihty, and history of past successes. However, certain important design criteria govern the proper appHcation of rules and there are shortcomings of the rule-based representation. 

Before any information can be entered into the knowledge base, decisions must be made about how the rules and data will be represented. The question of knowledge representation is a matter for the knowledge engineer who, from a preliminary discussion with the expert, and meetings with potential... 

Instead of representing knowledge in a static way, rule-based systems represent knowledge in terms of rules that lead to conclusions. A simple rule-based system consists of a set of if-then rules, a collection of facts, and an interpreter controlling the application of the rules by the given facts. Other important knowledge representation techniques are frames and semantic networks [1],... 

In a knowledge engineering process, the scientist would be interviewed and posed representative problems. Based on his responses, the knowledge he applies needs to be understood and encoded in the form of the knowledge representation used the rules. In a second stage, the scientist would then need to validate the programmed rules to make sure that the outcome from expert systems is valid in the problems domain. This cycle needs continuous repetition until the system performs in the desired manner. 

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