<P> Alpha--beta search can be made even faster by considering only a narrow search window (generally determined by guesswork based on experience). This is known as aspiration search . In the extreme case, the search is performed with alpha and beta equal; a technique known as zero - window search, null - window search, or scout search . This is particularly useful for win / loss searches near the end of a game where the extra depth gained from the narrow window and a simple win / loss evaluation function may lead to a conclusive result . If an aspiration search fails, it is straightforward to detect whether it failed high (high edge of window was too low) or low (lower edge of window was too high). This gives information about what window values might be useful in a re-search of the position . </P> <P> Over time, other improvements have been suggested, and indeed the Falphabeta (fail - soft alpha - beta) idea of John Fishburn is nearly universal and is already incorporated above in a slightly modified form . Fishburn also suggested a combination of the killer heuristic and zero - window search under the name Lalphabeta ("last move with minimal window alpha - beta search"). </P> <P> Stockfish (chess) is a C++ open source chess program that implements the Alpha Beta pruning algorithm . Stockfish is a mature implementation that is rated as one of the strongest chess engines available today as evidenced by it winning the Top Chess Engine Championship in 2016 and 2017 . </P> <P> Since the minimax algorithm and its variants are inherently depth - first, a strategy such as iterative deepening is usually used in conjunction with alpha--beta so that a reasonably good move can be returned even if the algorithm is interrupted before it has finished execution . Another advantage of using iterative deepening is that searches at shallower depths give move - ordering hints, as well as shallow alpha and beta estimates, that both can help produce cutoffs for higher depth searches much earlier than would otherwise be possible . </P>

Alpha beta pruning in artificial intelligence in hindi