# Alpha Beta Pruning

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Alpha-beta pruning is a popular optimization technique used in the minimax algorithm, which is a decision-making algorithm often employed in two-player, zero-sum games like chess, checkers, or tic-tac-toe. The goal of alpha-beta pruning is to reduce the number of nodes evaluated in the game tree, thereby improving the efficiency of the search process while still producing the same optimal move.

## MiniMax

The minimax algorithm explores the entire game tree by simulating all possible moves that players can make, alternating between maximizing and minimizing players at each level of the tree. The algorithm assigns a value to each possible move based on the assumption that both players play optimally. The maximizing player aims to maximize the score, while the minimizing player aims to minimize it.

## Alpha beta pruning optimizes MiniMax

Alpha-beta pruning takes advantage of the fact that, during the search process, we can determine certain branches of the game tree that we don't need to explore fully because we can already deduce that they will not lead to a better outcome. This is achieved by using two values: alpha and beta.

• Alpha (α): The best value that the maximizing player can guarantee so far.
• Beta (β): The best value that the minimizing player can guarantee so far.

The algorithm maintains these alpha and beta values as it traverses the game tree. As it explores nodes, it updates these values based on the values of previously explored nodes.

The key insight of alpha-beta pruning is that if at any point during the traversal, the algorithm discovers a move that is worse for the maximizing player than a move it has already considered, then the maximizing player will never choose that move. Similarly, if it discovers a move that is better for the minimizing player than a move it has already considered, then the minimizing player will never choose that move.

With this insight, the algorithm prunes (skips) the exploration of certain branches of the game tree, effectively reducing the number of nodes that need to be evaluated.

## Step-by-step

Here's a simplified step-by-step explanation of how the alpha-beta pruning algorithm works:

1. Start at the root of the game tree.
2. Perform a depth-first search, evaluating nodes and updating alpha and beta values.
3. When a maximizing player encounters a node with a value greater than or equal to beta, stop evaluating further nodes in this branch, as the minimizing player will never choose this branch.
4. When a minimizing player encounters a node with a value less than or equal to alpha, stop evaluating further nodes in this branch, as the maximizing player will never choose this branch.
5. Update alpha and beta values at each level to refine the search space.
6. Continue the traversal, applying steps 2-5 until the entire tree is traversed.

By pruning branches of the tree that are guaranteed to be suboptimal, the alpha-beta pruning algorithm significantly reduces the number of nodes evaluated, leading to a faster and more efficient search process while still finding the optimal move.

It's worth noting that the efficiency of alpha-beta pruning can vary based on factors such as the order in which nodes are evaluated and the quality of the game's evaluation function. Additionally, while alpha-beta pruning is most commonly associated with two-player, zero-sum games, variations of the technique can be applied to other search and optimization problems as well.

## Example in C

Here's a simple implementation of the alpha-beta pruning algorithm in ANSI C for a two-player, zero-sum game tree. This example assumes that the game tree nodes are represented by a structure called Node, and each node has a value associated with it. You can modify and adapt this code to your specific game and data structures.

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56``` ```#include #include // Structure to represent a node in the game tree struct Node { int value; // Add more fields relevant to your game }; // Function to perform alpha-beta pruning int alphaBetaPruning(struct Node node, int depth, int alpha, int beta, int isMaximizingPlayer) { if (depth == 0 /* Add other stopping conditions here */) { return node.value; // Evaluate the node's value using your evaluation function } if (isMaximizingPlayer) { int maxEval = INT_MIN; // Generate child nodes and iterate through them // Update maxEval and alpha based on evaluations for (...) { int eval = alphaBetaPruning(childNode, depth - 1, alpha, beta, 0); maxEval = eval > maxEval ? eval : maxEval; alpha = alpha > eval ? alpha : eval; if (beta <= alpha) { break; // Beta cutoff } } return maxEval; } else { int minEval = INT_MAX; // Generate child nodes and iterate through them // Update minEval and beta based on evaluations for (...) { int eval = alphaBetaPruning(childNode, depth - 1, alpha, beta, 1); minEval = eval < minEval ? eval : minEval; beta = beta < eval ? beta : eval; if (beta <= alpha) { break; // Alpha cutoff } } return minEval; } } int main() { // Initialize the root node and other necessary data struct Node rootNode; // Initialize other variables // Call alphaBetaPruning function to find the optimal move int optimalValue = alphaBetaPruning(rootNode, /* specify depth */, INT_MIN, INT_MAX, 1); printf("Optimal value: %d\n", optimalValue); return 0; } ```

Please note that this code is a simplified example, and you will need to adapt it to your specific game and data structures. You'll need to implement the actual generation of child nodes, evaluation function, and other game-specific logic within the placeholders in the code. Additionally, make sure to handle memory management, data structures, and other necessary details based on your game's requirements.

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