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Forward chaining (or forward reasoning) is one of the two main methods of reasoning when using an inference engine and can be described logically as repeated application of modus ponens. Forward chaining is a popular implementation strategy for expert systems, business and production rule systems. The opposite of forward chaining is backward chaining. Forward chaining starts with the available data and uses inference rules to extract more data (from an end user, for example) until a goal is reached. An inference engine using forward chaining searches the inference rules until it finds one where the antecedent (If clause) is known to be true. 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. Inference engines will iterate through this process until a goal is reached. Suppose that the goal is to conclude the color of a pet named Fritz, given that he croaks and eats flies, and that the rule base contains the following four rules: If X croaks and X eats flies - Then X is a frog If X chirps and X sings - Then X is a canary If X is a frog - Then X is green If X is a canary - Then X is blue Let us illustrate forward chaining by following the pattern of a computer as it evaluates the rules. Assume the following facts: Fritz croaks Fritz eats flies With forward reasoning, the inference engine can derive that Fritz is green in a series of steps:
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