Alpha–Beta pruning is a search algorithm that tries to reduce the number of nodes that are searched by the minimax algorithm in the search tree. It can be applied to ‘n’ depths and can prune the entire subtrees and leaves.
Alpha-Beta pruning is a technique used in artificial intelligence, specifically in game trees, to reduce the number of nodes that need to be evaluated in the search for the optimal move. It is an enhancement of the minimax algorithm, which exhaustively explores all possible moves in a game tree to determine the best move.
Here’s a simplified explanation of how Alpha-Beta pruning works:
- Minimax Algorithm: In the minimax algorithm, the goal is to find the best move for a player assuming that the opponent also plays optimally. It involves recursively exploring the game tree, alternating between maximizing and minimizing the possible outcomes until a terminal state (win, lose, or draw) is reached.
- Alpha-Beta Pruning: Alpha-Beta pruning enhances the minimax algorithm by introducing two additional parameters, alpha and beta, to keep track of the best moves found so far for the maximizing and minimizing players, respectively.
- Alpha represents the best score found so far for the maximizing player (initially negative infinity).
- Beta represents the best score found so far for the minimizing player (initially positive infinity).
- Pruning: During the recursive search, if it’s found that a branch (partial move sequence) can never lead to a better outcome than a previously examined branch, it can be pruned, i.e., cut off from further exploration. This is possible because the maximizing player only cares about moves that improve their position (alpha), while the minimizing player only cares about moves that worsen the maximizing player’s position (beta).
- If the score of a node exceeds beta for a minimizing player or falls below alpha for a maximizing player, then the remaining sub-tree can be pruned.
- This significantly reduces the number of nodes that need to be evaluated, thus improving the efficiency of the search.
- Efficiency: Alpha-Beta pruning allows for a significant reduction in the number of nodes explored, particularly in games with a large search space like chess or checkers, making it feasible to search deeper into the game tree within a reasonable amount of time.
In summary, Alpha-Beta pruning is a crucial optimization technique for minimizing the number of nodes evaluated in the search for the optimal move, thereby improving the efficiency of game-playing algorithms in artificial intelligence.