Expert inference engine

The classical architecture of an expert system comprises a knowledge base, an inference engine, and some kind of user interface. Most expert systems also include an explanation subsystem and a knowledge acquisition subsystem. This architecture is given in Figure 9-34 and described in more detail below. 

The typical structure of an expert system is shown in Fig. 43.1. Three basic components are present in all expert systems the knowledge base, the inference engine and the interaction module (user interface). The knowledge base is the heart of the expert system. It contains the necessary expert knowledge and experience to act as a decision support. Only if this is correct and complete enough the expert system can produce meaningful and useful conclusions and advice. The inference... 

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 this example this combination is straightforward. One has to be aware, however, that a medium-sized expert system easily contains several hundreds of rules. In addition, several rules can be valid at the same time. The inference engine should also have a strategy for deciding on priorities conflict resolution). 

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 ... 

SpinPro is a typical backward chaining, rule-based expert system. Rule-based systems are systems in which the expert s knowledge is encoded primarily in the form of if-then rules, i.e., if a set of conditions are found to be true then draw a conclusion or perform an action. "Backward chaining" refers to the procedure for finding a solution to a problem. In a backward chaining system, the inference engine works backwards from a hypothesized solution to find facts that support the hypothesis. Alternative hypotheses are tried until one is found that is supported by the facts. 

Expert System Shells are software packages that facilitate the building of knowledge-based systems by providing a knowledge representation scheme and an inference engine. The developer adds domain knowledge. 

Inference Engine is the processing portion of an expert system. With information from the knowledge base, the inference engine provides the reasoning ability that derives inferences, or conclusions, on which the expert system acts. 

Expert System Shell is a suite of software that allows construction of a knowledge base and interaction through use of an inference engine. 

Inference Engine is a module of an expert system that performs reasoning using rules from the knowledge base and facts from the working memory to process them according to specihed business rules. 

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