Forward chaining in AI is a method in which inference rules are applied to existing data to extract additional data until an end goal is achieved.
Backward chaining in AI is the logical process of inferring unknown truths from known conclusions by moving backward from a solution to determining the initial conditions.
Forward Chaining VS Backward Chaining
|Forward Chaining||Backward Chaining|
|Forward chaining starts with the available data and uses inference rules to extract more data until a goal is reached.||Backward chaining is an inference method that can be described as working backward from the goals.|
|Forward chaining starts with available data.||Backward chaining starts with the list of goals.|
|Forward chaining is also known as a Data-Driven inference technique.||Backward chaining is also known as the Goal-Driven inference technique.|
|It can create an infinite number of possible conclusions.||It only produces a reasonable amount of final conclusions.|
|It is bottom-up reasoning.||It is top-down reasoning.|
|Forward chaining is Breadth First Search Approach.||Backward chaining is Depth First Search Approach.|
|In forward chaining, the process is present to future.||In backward chaining, the process is present to past.|
|It operates in the forward direction(antecedent to consequence).||It operates in the backward direction(consequence to antecedent).|
|In forward chaining, the antecedent determines where the search goes.||In backward chaining, consequence determines search.|
|Explanations are not easily facilitated.||Explanations are facilitated.|
|Forward chaining works forward to find what solutions follow from the facts.||Backward chaining works backward to find facts that support the hypothesis.|
|Forward chaining is used for tasks such as planning, design process monitoring, diagnosis, and classification||Backward chaining is used in automated theorem provers, proof assistants, and other artificial intelligence applications. It is only used for classification and diagnosis tasks.|