"Backward chaining (or backward reasoning) is an inference method that can be described colloquially as working backward from the goal(s). It is used in automated theorem provers, inference engines, proof assistants and other artificial intelligence applications. In game theory, its application to (simpler) subgames in order to find a solution to the game is called backward induction. In chess, it is called retrograde analysis, and it is used to generate tablebases for chess endgames for computer chess. Backward chaining is implemented in logic programming by SLD resolution. Both rules are based on the modus ponens inference rule. It is one of the two most commonly used methods of reasoning with inference rules and logical implications – the other is forward chaining. Backward chaining systems usually employ a depth-first search strategy, e.g. Prolog"

James Clear Mental Models Overview