What is Alpha Beta pruning in AI?

Alpha-Beta Pruning is a search algorithm designed to reduce the number of nodes that must be evaluated during the search in adversarial games such as chess and Go. This algorithm is based on the Minimax algorithm and reduces the size of the search tree by pruning nodes that cannot influence the final decision. In simple terms, Alpha-Beta Pruning provides a way for search engines to understand 'if I take this action, what responses might the opponent have?' and then select the optimal strategy based on these responses. Alpha-Beta Pruning is primarily implemented through two parameters: Alpha and Beta. Alpha represents the lower bound of the best known choice that the current player can guarantee during the search, while Beta represents the upper bound of the best known choice that the opponent can guarantee. During the search, if a node's potential outcome is found to be worse than the current Alpha or Beta, it can be skipped along with all its child nodes. For example, in chess, with Alpha-Beta Pruning, if analyzing a move reveals that the player's best possible outcome is worse than previously analyzed options, there is no need to continue exploring this move because the opponent would not allow you to reach an outcome unfavorable to them. This method significantly improves search efficiency by avoiding unnecessary searches of paths that are clearly not selected, thereby saving substantial computational resources and time.