国际象棋的alhpabeta剪枝极小极大

Question

我正在使用带有alpha-beta修剪的minimax方法创建一个国际象棋AI。我试图理解alpha-beta剪枝是如何工作的，但当涉及到设置特定搜索深度的国际象棋时，我无法理解它

带alpha-beta的minimax如何解决为优势牺牲一块2-3步的问题？难道它不会仅仅看到牺牲的位置，并立即将该树枝视为坏树枝丢弃，从而错过好的“牺牲”吗

感谢您对改进的任何澄清或建议。以下是我目前的代码：

def minimax(board, depth, alpha, beta, maximizing_player):

    board.is_human_turn = not maximizing_player
    children = board.get_all_possible_moves()

    if depth == 0 or board.is_draw or board.is_check_mate:
        return None, evaluate(board)

    best_move = random.choice(children)

    if maximizing_player:
        max_eval = -math.inf
        for child in children:
            board_copy = copy.deepcopy(board)
            board_copy.move(child)
            current_eval = minimax(board_copy, depth - 1, alpha, beta, False)[1]
            if current_eval > max_eval:
                max_eval = current_eval
                best_move = child
            alpha = max(alpha, current_eval)
            if beta <= alpha:
                break
        return best_move, max_eval

    else:
        min_eval = math.inf
        for child in children:
            board_copy = copy.deepcopy(board)
            board_copy.move(child)
            current_eval = minimax(board_copy, depth - 1, alpha, beta, True)[1]
            if current_eval < min_eval:
                min_eval = current_eval
                best_move = child
            beta = min(beta, current_eval)
            if beta <= alpha:
                break
        return best_move, min_eval