When such a rule is found, the engine can conclude, or infer, the consequent (Then clause), resulting in the addition of new information to its data.
Assume the following facts: With forward reasoning, the inference engine can derive that Fritz is green in a series of steps: 1.
Because the data determines which rules are selected and used, this method is called data-driven, in contrast to goal-driven backward chaining inference.
One of the advantages of forward-chaining over backward-chaining is that the reception of new data can trigger new inferences, which makes the engine better suited to dynamic situations in which conditions are likely to change.
[2][3] Forward chaining is a powerful reasoning strategy with numerous applications in AI and related fields.