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Structure of Expert Systems
Inference engine
The inference engine connects the knowledge base and the database of facts. It interprets the rules and draws conclusions. With rule based expert systems there are two main types of reasoning - forward chaining and backward chaining. The user interface for both of these systems may be similar, it is how they use the rules that is different. Some expert systems use both backward and forward chaining.
Forward chaining
Forward chaining is a 'data driven' method of reasoning. It begins with the available data, compares it with the facts and rules held in the knowledge base and then infers or draws the most likely conclusion. IF
THEN. Forward chaining starts with the symptoms and works forward to find a solution.
Backward chaining
Backward chaining is a 'goal driven' method of reasoning. It begins with a goal and then looks at the evidence (data and rules) to determine whether or not it is correct. THEN
IF. Backward chaining starts with a hypothesis and works backwards to prove or disprove it.
Fuzzy logic
In expert systems there are usually many different variables, so how does the system know which are the most or least important? Fuzzy logic allows expert systems to reach decisions when there are no completely true or completely false answers. It is based
on the way that humans make decisions.
Each variable is assigned a value between 0 and 1, such as 0.4 or 40%, the variables can then be processed by the system to reach a decision. In our Animal-ID system for example, the user or may not know whether the animal has pointed teeth and so therefore most but not all rules would match and the certainty value would be reduced to say 0.8.
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