Expert Systems/Components of Expert Systems
Components of an Expert System
An expert system is typically composed of at least three primary components. These are the inference engine, the knowledge base, and the User interface. We will introduce these components below.
The knowledge base is a collection of rules or other information structures derived from the human expert. Rules are typically structured as If/Then statements of the form:
IF <antecedent> THEN <consequent>
The antecedent is the condition that must be satisfied. When the antecedent is satisfied, the rule is triggered and is said to "fire". The consequent is the action that is performed when the rule fires.
When rules are satisfied by the program, they are added to a queue called the agenda. The agenda is an unordered list of all the rules whose antecedents are currently satisfied.
Knowledge bases are typically not ordered, because order tends to play very little role in an expert system. Rules may be placed on the agenda in any order, and they may be fired in any order once they are on the agenda.
The inference engine is the main processing element of the expert system. The inference engine chooses rules from the agenda to fire. If there are no rules on the agenda, the inference engine must obtain information from the user in order to add more rules to the agenda. It makes use of knowledge base, in order to draw conclusions for situations. It is responsible for gathering the information from the user, by asking various questions and applying it wherever necessary.
A user interface is the method by which the expert system interacts with a user. These can be through dialog boxes, command prompts, forms, or other input methods. Some expert systems interact with other computer applications, and do not interact directly with a human. In these cases, the expert system will have an interaction mechanism for transactions with the other application, and will not have a user interface.
There are other components that are relatively common in an expert system, but are not strictly needed.
Working memory contains the data that is received from the user during the expert system session. Values in working memory are used to evaluate antecedents in the knowledge base. Consequences from rules in the knowledge base may create new values in working memory, update old values, or remove existing values.
The method by which an expert system reaches a conclusion may not be obvious to a human user, so many expert systems will include a method for explaining the reasoning process that lead to the final answer of the system.