Alpha-Beta pruning is an optimization technique used with the Minimax algorithm in game-playing artificial intelligence. It reduces the number of nodes that need to be evaluated without changing the final decision of the Minimax algorithm. The technique maintains two values, called alpha and beta. Alpha represents the best value that the MAX player can guarantee at the current point, while beta represents the best value that the MIN player can guarantee. Whenever the condition α ≥ β becomes true, the remaining unexplored branches of that node can be safely ignored because they cannot influence the final decision. To apply Alpha-Beta pruning to the given graph, the graph must be traversed from the root node toward the leaf nodes in the specified left-to-right order. The root is normally considered a MAX node, and its children are MIN nodes. The levels then alternate between MAX and MIN until the terminal or leaf nodes are reached. The values at the leaf nodes are propagated upward according to the Minimax rules, while alpha and beta values are updated at every node. At a MAX node, the algorithm attempts to maximize the value. Initially, alpha is set to −∞ and beta is set to +∞. Whenever a child produces a value greater than the current alpha, alpha is updated. At a MIN node, the algorithm attempts to minimize the value. Therefore, whenever a child produces a value smaller than the current beta, beta is updated. The pruning rule is particularly important. At any point during the traversal, if the value of alpha becomes greater than or equal to beta, the remaining children of the current node do not need to be examined. These children are the