When should a branch be pruned in alpha-beta pruning?

In the context of alpha-beta pruning, pruning occurs when the value of alpha exceeds the value of beta. This situation indicates that the current branch cannot produce a better outcome than previously explored branches. In games or decision trees, if you are minimizing the possible score for a player, and you find a move that results in a score worse than what has already been identified for maximizing player (alpha), any further exploration of this branch is unnecessary. This is because it will not lead to an optimal decision for the player trying to maximize their score (or minimize their loss). Therefore, when alpha is greater than beta, it is a clear signal that the other branch being considered has already had a potential outcome that is better.

While the other choices involve valid conditions in a decision-making context, they do not align with the specific criteria for pruning in alpha-beta pruning. The maximum depth (choice A) does not influence pruning directly; it relates more to algorithm termination. Similarly, finding a solution (choice C) is a different concept, as pruning is about efficiency and not necessarily about confirming a solution. Evaluating a node fully (choice D) relates to determining a value but does not directly trigger the pruning decision. Thus, the condition for pruning directly applies to the