alpha-beta pruning

Alpha-beta pruning works best when the best moves are considered first.

For example, alpha-beta pruning is most efficient if it searches the best moves first.

Since he had only a very limited amount of available computer memory, Samuel implemented what is now called alpha-beta pruning.

In 1963 Brudno published his work on alpha-beta pruning.

It is an alternative to the alpha-beta pruning algorithm.

There is no report of how well this system performed compared to alpha-beta pruning search engines running on the same hardware.

In theory, endgames are a game of perfect information, so the Alpha-beta pruning algorithm should work.

Furthermore, the usefulness of alpha-beta pruning is heavily dependent on the order in which moves are considered.

NegaScout is a negamax algorithm that can be faster than alpha-beta pruning.

The benefit of alpha-beta pruning lies in the fact that branches of the search tree can be eliminated.

For this reason, branch-and-bound techniques are often used in game tree search algorithms, most notably through the use of alpha-beta pruning.

Brudno's work on alpha-beta pruning was published in 1963 in Russian and English.

In Arimaa software the speedup provided by alpha-beta pruning is less, because captures are rarer.

When evaluating a large game tree using techniques such as minimax or alpha-beta pruning, search depth is limited for feasibility reasons.

SparkChess uses alpha-beta pruning with principal variation, iterative deepening and aspiration window.

More sophisticated search techniques such as alpha-beta pruning are able to eliminate entire subtrees of the search tree from consideration.

Pseudocode for depth-limited negamax search with alpha-beta pruning:

It also employed usual algorithms as found in computer chess systems: alpha-beta pruning with move ordering, transposition tables, etc.

Alexander Reinefeld invented NegaScout several decades after the invention of alpha-beta pruning.

Using 10.000 games with Alpha-beta pruning players the game-tree complexity and state-space complexity can be computed.

In such search problems, a heuristic can be used to try good choices first so that bad paths can be eliminated early (see alpha-beta pruning).

In alpha-beta pruning, the search is fastest (in fact, optimal) when the child of a node corresponding to the best move is always considered first.

The performance of the naïve minimax algorithm may be improved dramatically, without affecting the result, by the use of alpha-beta pruning.

Hydra's search used alpha-beta pruning as well as null-move heuristics [1].

In computer chess programs, the null-move heuristic is a heuristic technique used to enhance the speed of the alpha-beta pruning algorithm.