请用脚本实现3种五子棋AI算法

为了实现三种五子棋 AI 算法，我将分别介绍极大极小值算法、Alpha-Beta 剪枝算法和蒙特卡洛树搜索算法的实现方法。以下是 Python 代码实现：

1. 极大极小值算法

def minimax(board, depth, is_maximizing_player):
    if depth == 0 or board.is_game_over():
        return None, evaluate(board)

    best_move = None
    if is_maximizing_player:
        max_score = -float('inf')
        for move in board.get_legal_moves():
            board.push(move)
            _, score = minimax(board, depth - 1, False)
            board.pop()
            if score > max_score:
                max_score = score
                best_move = move
        return best_move, max_score
    else:
        min_score = float('inf')
        for move in board.get_legal_moves():
            board.push(move)
            _, score = minimax(board, depth - 1, True)
            board.pop()
            if score < min_score:
                min_score = score
                best_move = move
        return best_move, min_score

2. Alpha-Beta 剪枝算法

def alphabeta(board, depth, alpha, beta, is_maximizing_player):
    if depth == 0 or board.is_game_over():
        return None, evaluate(board)

    best_move = None
    if is_maximizing_player:
        max_score = -float('inf')
        for move in board.get_legal_moves():
            board.push(move)
            _, score = alphabeta(board, depth - 1, alpha, beta, False)
            board.pop()
            if score > max_score:
                max_score = score
                best_move = move
            alpha = max(alpha, max_score)
            if beta <= alpha:
                break
        return best_move, max_score
    else:
        min_score = float('inf')
        for move in board.get_legal_moves():
            board.push(move)
            _, score = alphabeta(board, depth - 1, alpha, beta, True)
            board.pop()
            if score < min_score:
                min_score = score
                best_move = move
            beta = min(beta, min_score)
            if beta <= alpha:
                break
        return best_move, min_score

3. 蒙特卡洛树搜索算法

class Node:
    def __init__(self, board, move=None, parent=None):
        self.board = board
        self.move = move
        self.parent = parent
        self.wins = 0
        self.visits = 0
        self.children = []

    def add_child(self, move, board):
        node = Node(board, move, self)
        self.children.append(node)
        return node

    def select_child(self):
        return max(self.children, key=lambda c: c.wins/c.visits + sqrt(2*log(self.visits)/c.visits))

    def update(self, result):
        self.visits += 1
        self.wins += result

def simulate(board):
    current_player = board.current_player
    while not board.is_game_over():
        move = random.choice(board.get_legal_moves())
        board.push(move)
    winner = board.get_winner()
    if winner == current_player:
        return 1
    elif winner == board.get_opponent(current_player):
        return -1
    else:
        return 0

def monte_carlo_tree_search(board, iterations):
    root_node = Node(board)
    for i in range(iterations):
        node = root_node
        temp_board = board.copy()
        while node.children:
            node = node.select_child()
            temp_board.push(node.move)

        unvisited_moves = temp_board.get_legal_moves() - {child.move for child in node.children}
        if unvisited_moves:
            move = random.choice(list(unvisited_moves))
            temp_board.push(move)
            node = node.add_child(move, temp_board)

        result = simulate(temp_board)
        while node:
            node.update(result)
            node = node.parent

    best_move = max(root_node.children, key=lambda c: c.wins/c.visits).move
    return best_move

以上是三种不同的五子棋 AI 算法的 Python 实现，可以根据实际需求选择其中一种或多种算法进行使用。