可以帮我实现五子棋的人工智能代码吗

当然可以！以下是一个基于 Python 语言实现的五子棋人工智能代码：

import numpy as np

# 定义棋盘大小
BOARD_SIZE = 15

# 定义棋盘状态
EMPTY = 0
BLACK = 1
WHITE = 2

# 定义评估函数参数
SCORE = [0, 1, 10, 100, 1000]

# 极小化极大算法
def minimax(board, depth, alpha, beta, is_max):
    # 判断是否达到搜索深度或者出现胜负
    if depth == 0 or is_win(board):
        return evaluate(board)

    # 极大节点
    if is_max:
        max_eval = -np.inf
        for i in range(BOARD_SIZE):
            for j in range(BOARD_SIZE):
                if board[i][j] == EMPTY:
                    board[i][j] = WHITE
                    eval = minimax(board, depth-1, alpha, beta, False)
                    board[i][j] = EMPTY
                    max_eval = max(max_eval, eval)
                    alpha = max(alpha, eval)
                    if beta <= alpha:
                        break
        return max_eval

    # 极小节点
    else:
        min_eval = np.inf
        for i in range(BOARD_SIZE):
            for j in range(BOARD_SIZE):
                if board[i][j] == EMPTY:
                    board[i][j] = BLACK
                    eval = minimax(board, depth-1, alpha, beta, True)
                    board[i][j] = EMPTY
                    min_eval = min(min_eval, eval)
                    beta = min(beta, eval)
                    if beta <= alpha:
                        break
        return min_eval

# Alpha-Beta 剪枝算法
def alphabeta(board, depth, alpha, beta, is_max):
    # 判断是否达到搜索深度或者出现胜负
    if depth == 0 or is_win(board):
        return evaluate(board)

    # 极大节点
    if is_max:
        max_eval = -np.inf
        for i in range(BOARD_SIZE):
            for j in range(BOARD_SIZE):
                if board[i][j] == EMPTY:
                    board[i][j] = WHITE
                    eval = alphabeta(board, depth-1, alpha, beta, False)
                    board[i][j] = EMPTY
                    max_eval = max(max_eval, eval)
                    alpha = max(alpha, eval)
                    if beta <= alpha:
                        break
        return max_eval

    # 极小节点
    else:
        min_eval = np.inf
        for i in range(BOARD_SIZE):
            for j in range(BOARD_SIZE):
                if board[i][j] == EMPTY:
                    board[i][j] = BLACK
                    eval = alphabeta(board, depth-1, alpha, beta, True)
                    board[i][j] = EMPTY
                    min_eval = min(min_eval, eval)
                    beta = min(beta, eval)
                    if beta <= alpha:
                        break
        return min_eval

# 启发式搜索
def heuristic(board):
    # 构建搜索队列
    queue = []
    for i in range(BOARD_SIZE):
        for j in range(BOARD_SIZE):
            if board[i][j] == EMPTY:
                score = evaluate_position(board, i, j)
                queue.append((i, j, score))

    # 根据评分排序
    queue.sort(key=lambda x: x[2], reverse=True)

    # 返回评分最高的位置
    return queue[0][0], queue[0][1]

# 评估函数
def evaluate(board):
    score = 0
    for i in range(BOARD_SIZE):
        for j in range(BOARD_SIZE):
            if board[i][j] == BLACK:
                score -= evaluate_position(board, i, j)
            elif board[i][j] == WHITE:
                score += evaluate_position(board, i, j)
    return score

# 位置评估函数
def evaluate_position(board, x, y):
    score = 0
    for dx in range(-1, 2):
        for dy in range(-1, 2):
            if dx == 0 and dy == 0:
                continue
            score += evaluate_direction(board, x, y, dx, dy)
    return score

# 方向评估函数
def evaluate_direction(board, x, y, dx, dy):
    count = 0
    is_blocked = False
    for i in range(5):
        nx = x + i * dx
        ny = y + i * dy
        if nx < 0 or ny < 0 or nx >= BOARD_SIZE or ny >= BOARD_SIZE:
            is_blocked = True
            break
        if board[nx][ny] == BLACK:
            count += 1
        elif board[nx][ny] == WHITE:
            is_blocked = True
            break
    if not is_blocked:
        score = SCORE[count]
    else:
        score = SCORE[count] // 2
    return score

# 判断是否出现胜负
def is_win(board):
    for i in range(BOARD_SIZE):
        for j in range(BOARD_SIZE):
            if board[i][j] != EMPTY:
                if check_direction(board, i, j, 1, 0) or \
                   check_direction(board, i, j, 0, 1) or \
                   check_direction(board, i, j, 1, 1) or \
                   check_direction(board, i, j, 1, -1):
                    return True
    return False

# 检查方向是否出现连续五子
def check_direction(board, x, y, dx, dy):
    count = 1
    for i in range(1, 5):
        nx = x + i * dx
        ny = y + i * dy
        if nx < 0 or ny < 0 or nx >= BOARD_SIZE or ny >= BOARD_SIZE:
            break
        if board[nx][ny] == board[x][y]:
            count += 1
        else:
            break
    for i in range(1, 5):
        nx = x - i * dx
        ny = y - i * dy
        if nx < 0 or ny < 0 or nx >= BOARD_SIZE or ny >= BOARD_SIZE:
            break
        if board[nx][ny] == board[x][y]:
            count += 1
        else:
            break
    if count >= 5:
        return True
    else:
        return False

在上述代码中，我们实现了极小化极大算法、Alpha-Beta 剪枝算法和启发式搜索等算法，同时也实现了评估函数、位置评估函数和方向评估函数等函数，来评估棋局的优劣程度。