Inferences engine

In order to make decisions on which a user can depend, the ES must have facts and rules. As we have seen above, production rules contain both a condition and an action part. 

IF the patient s Body Mass Index is well above recommended levels  

THEN recommend weight loss to reduce the risk of a heart attack  

The condition part is a testable statement whose truth may be determined by inspection of data within the knowledge base or provided by the user in response to questions from the ES. The data consist of clear statements of fact  

More than four inches of rain fell in Boston during April. 

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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. 

Inference engine The inference engine represents the central problem-solving subsystem. It contains strategies for using the information contained in the... 

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). 

An interpreter, or explanation system, forms the interface between the user and the core software. It interprets queries from the user so that the inference engine can make sense of them and presents the conclusions of the inference engine in language appropriate to the needs and level of knowledge of the user. It is also responsible for generating explanations of the reasoning that... 

In backward chaining, the inference engine would search through the knowledge base until it found a rule, the conclusion of which was "Go to live with mother." It would find three such rules ... 

In a goal-driven system, the inference engine would start with the first rule because the conclusion of that rule is the recommendation of a solvent. 

The user interface, the interpreter, and the inference engine together comprise the ES shell or a skeletal system. As these components are largely independent of the specific application, all that is needed to create a working ES from the shell is to feed it with rules and facts. 

Within a fuzzy system, an inference engine works with fuzzy rules it takes input, part of which may be fuzzy, and generates output, some or all of which may be fuzzy. Although the role of a fuzzy system is to deal with uncertain data, the input is itself not necessarily fuzzy. For example, the data fed into the system might consist of the pH of a solution or the molecular weight of a compound, both of which can be specified with minimal uncertainty. In addition, the output that the system is required to produce is of more value if it is provided in a form that is crisp "Set the thermostat to 78°C" is more helpful to a scientist than "raise the temperature of the oven." Consequently, the fuzzy core of the inference engine is bracketed by one step that can turn crisp data into fuzzy data, and another that does the reverse. 

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