backwardchained

Backward chaining, also known as goal-driven inference, is a reasoning procedure used in AI, logic programming, and rule-based expert systems. It starts with a desired conclusion or goal and works backward to determine whether there are facts or rules that support it. The process seeks a chain of premises whose satisfaction would lead to the goal being true.

In practice, backward chaining selects rules whose consequent matches the current goal. For each such rule, it attempts to prove every antecedent. If an antecedent is itself a subgoal, the system recursively applies the same method. The procedure may backtrack to explore alternative rules when a subgoal cannot be proved. In logic programming, this approach is often implemented by procedures like SL resolution, and languages such as Prolog embody backward-chaining inference as a core mechanism.

Backward chaining is contrasted with forward chaining, which starts from known facts and applies rules to derive new information. Backward chaining can be more efficient when the goal is specific and the data set is large, as it avoids materializing all possible inferences. However, its performance depends on rule structure, goal complexity, and the presence of cycles or recursive rules.

Common applications include diagnostic systems, theorem proving, and certain knowledge-based expert systems. It is also a foundational concept in logic programming and goal-driven query answering. While powerful for targeted questions, practitioners often combine backward and forward approaches to leverage the strengths of both methods.