Editing Alpha–beta pruning (section)

== Heuristic improvements ==
Further improvement can be achieved without sacrificing accuracy by using ordering [[heuristic]]s to search earlier parts of the tree that are likely to force alpha–beta cutoffs. For example, in chess, moves that capture pieces may be examined before moves that do not, and moves that have scored highly in [[Iterative deepening depth-first search|earlier passes]] through the game-tree analysis may be evaluated before others. Another common, and very cheap, heuristic is the [[killer heuristic]], where the last move that caused a beta-cutoff at the same tree level in the tree search is always examined first. This idea can also be generalized into a set of [[refutation table]]s.

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 an ''[[aspiration window]]''. In the extreme case, the search is performed with alpha and beta equal; a technique known as ''[[MTD(f)#Zero-window searches|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.

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").