Alpha_beta_pruning_using_array.py

import math

def minimax_alpha_beta(node, depth, alpha, beta, is_maximizing):
    if depth == 0 or is_terminal(node):
        return evaluate(node)
    
    if is_maximizing:
        value = -math.inf
        for child in generate_children(node):
            value = max(value, minimax_alpha_beta(child, depth - 1, alpha, beta, False))
            alpha = max(alpha, value)
            if beta <= alpha:
                break  # Prune the remaining branches
        return value
    else:
        value = math.inf
        for child in generate_children(node):
            value = min(value, minimax_alpha_beta(child, depth - 1, alpha, beta, True))
            beta = min(beta, value)
            if beta <= alpha:
                break  # Prune the remaining branches
        return value

def is_terminal(node):
    # Define your terminal condition here
    pass

def evaluate(node):
    # Define your evaluation function here
    pass

def generate_children(node):
    # Generate the children nodes of the current node
    pass

# Example usage
if __name__ == "__main__":
    tree = [3, 5, 6, 9, 1, 2, 0, 8]  # Replace with your tree structure

    result = minimax_alpha_beta(tree, depth=3, alpha=-math.inf, beta=math.inf, is_maximizing=True)
    print("Optimal value:", result)