Alpha-Beta pruning can change the final decision made by the Minimax algorithm.

Alpha-Beta pruning is an optimization technique for the Minimax algorithm that significantly reduces the number of nodes evaluated in the search tree. Here’s a detailed breakdown of how it impacts the decision-making process:

1. Understanding the Minimax Algorithm: The Minimax algorithm is used in decision-making and game theory, particularly in two-player games. It works by minimizing the possible loss for a worst-case scenario. When dealing with a minimax tree, the algorithm traverses from the leaves of the tree to the root, maximizing the score for the maximizing player and minimizing it for the minimizing player.

2. Introduction to Alpha-Beta Pruning: Alpha-Beta pruning enhances the Minimax algorithm by "pruning" away branches that don't need to be evaluated because they cannot possibly influence the final decision. It maintains two values, alpha and beta:

   • Alpha represents the best value that the maximizing player currently can guarantee at that level or above.
   • Beta represents the best value that the minimizing player can guarantee at that level or above.

3. How Pruning Works:

   • As the algorithm traverses the tree, it updates the alpha and beta values based on what it finds. If at any point the algorithm discovers that a branch cannot possibly influence the final decision (for instance, if it finds a move that is worse than a previous move for the maximizing player), it stops evaluating that branch.
   • By cutting off parts of the tree that do not need to be explored, Alpha-Beta pruning allows the algorithm to reach solutions faster.

4. Impact on Final Decisions:

   • While Alpha-Beta pruning does not affect the final decision when it comes to choosing the optimal move, it can lead to faster computations, allowing deeper searches within the same time frame. This means that the algorithm can consider more moves or a deeper level of the tree, potentially leading to different contexts where strategies could be evaluated more thoroughly. However, under the same search limit and conditions, the optimal move chosen will remain the same.

5. Conclusion: In summary, Alpha-Beta pruning increases the efficiency of the Minimax algorithm by reducing the number of nodes evaluated without changing the end results in terms of optimal decisions. It maintains the integrity of the Minimax approach while providing significant computational advantages.