Editing Forward chaining (section)

{{Short description|Inference engine in an expert system}}
{{about|forward chaining inference engines|forward chaining as an instructional procedure|Chaining}}
'''Forward chaining'''  (or '''forward reasoning''') is one of the two main methods of [[Automated reasoning|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 system]]s, [[business rules engine|business]] and [[Production system (computer science)|production rule systems]]. The opposite of forward chaining is [[backward chaining]].

Forward chaining starts with the available data and uses [[inference rule]]s 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 (logic)|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.<ref>{{cite book|last=Feigenbaum|first=Edward|title=The Rise of the Expert Company|url=https://archive.org/details/riseofexpertco00feig|url-access=registration|year=1988|publisher=Times Books|isbn=0-8129-1731-6|page=[https://archive.org/details/riseofexpertco00feig/page/318 318]}}</ref>  
 
Inference engines will [[Iteration#Computing|iterate]] through this process until a goal is reached.