Forward chaining. This method begins with a set of known facts or attribute values and applies these values to rules that use them in their premise. Any rules that are proven true fire and produce additional facts that are again applied to relevant rules. The process continues until no new facts are produced or a value for the goal is obtained. This approach works well when it is natural to gather multiple facts before trying to draw any conclusions and when there are many possible conclusions to be drawn from the facts.

Backward chaining. An alternative approach begins with a rule that could conclude the goal for the consultation ("what action do you recommend to get my car to start?"), tries to obtain values for the attributes used in the rule's premise, then backtracks through additional rules if necessary to determine a value of the goal attribute. When there are many attributes employed in many rules, the backward chaining mechanism produces a more efficient interview than forward chaining because it will not be necessary to ask the user to input values of all of the facts.