alpha beta剪枝是一种用于优化博弈树搜索算法的技术，启发式函数可以用来估计当前局面的价值，从而在搜索中提供更好的剪枝机会。下面是一个使用alpha beta剪枝和启发式函数来解决博弈问题的示例代码：

# 启发式函数，用于评估当前局面的价值，返回一个评估值
def heuristic(board):
    # 根据具体问题设计启发式函数
    # 这里假设当前局面的价值为黑子数量减去白子数量
    black_count = sum([1 for row in board for cell in row if cell == 'B'])
    white_count = sum([1 for row in board for cell in row if cell == 'W'])
    return black_count - white_count

# alpha beta剪枝函数，返回最佳行动和对应的评估值
def alpha_beta(board, depth, alpha, beta, maximizing_player):
    if depth == 0 or game_over(board):
        return None, heuristic(board)

    if maximizing_player:
        max_eval = float('-inf')
        best_action = None
        for action in get_possible_actions(board):
            new_board = make_move(board, action)
            _, eval = alpha_beta(new_board, depth - 1, alpha, beta, False)
            if eval > max_eval:
                max_eval = eval
                best_action = action
            alpha = max(alpha, eval)
            if beta <= alpha:
                break  # beta剪枝
        return best_action, max_eval
    else:
        min_eval = float('inf')
        best_action = None
        for action in get_possible_actions(board):
            new_board = make_move(board, action)
            _, eval = alpha_beta(new_board, depth - 1, alpha, beta, True)
            if eval < min_eval:
                min_eval = eval
                best_action = action
            beta = min(beta, eval)
            if beta <= alpha:
                break  # alpha剪枝
        return best_action, min_eval

# 在主函数中调用alpha beta剪枝函数
def main():
    board = [['B', 'B', 'W'],
             ['W', 'B', 'W'],
             ['B', 'W', 'B']]
    depth = 3
    alpha = float('-inf')
    beta = float('inf')
    maximizing_player = True

    best_action, eval = alpha_beta(board, depth, alpha, beta, maximizing_player)

    print("Best action:", best_action)
    print("Evaluation value:", eval)

if __name__ == '__main__':
    main()

这个示例代码展示了如何使用alpha beta剪枝和启发式函数来解决博弈问题。其中heuristic函数用于评估当前局面的价值，alpha_beta函数使用alpha beta剪枝算法进行搜索，最后在main函数中调用alpha_beta函数来找到最佳行动和对应的评估值。