Python 围棋 AI 代码：使用 Minimax 算法实现简单 AI

以下是一个简单的围棋AI的代码示例：\n\npython\nimport numpy as np\n\nBOARD_SIZE = 9\n\n# 定义棋盘状态\nEMPTY = 0\nBLACK = 1\nWHITE = -1\n\n# 初始化棋盘\nboard = np.zeros((BOARD_SIZE, BOARD_SIZE), dtype=int)\n\ndef get_valid_moves(board, player):\n valid_moves = []\n for i in range(BOARD_SIZE):\n for j in range(BOARD_SIZE):\n if board[i, j] == EMPTY:\n valid_moves.append((i, j))\n return valid_moves\n\ndef make_move(board, move, player):\n board[move] = player\n\ndef is_valid_move(board, move):\n return board[move] == EMPTY\n\ndef is_game_over(board):\n # 判断是否有一方获胜或者棋盘已满\n for i in range(BOARD_SIZE):\n for j in range(BOARD_SIZE):\n if board[i, j] == EMPTY:\n return False\n return True\n\ndef evaluate(board, player):\n # 评估当前棋盘局势\n # 这里简单地以黑方棋子数减去白方棋子数作为评估值\n return np.sum(board == player) - np.sum(board == -player)\n\ndef minimax(board, depth, player):\n if depth == 0 or is_game_over(board):\n return evaluate(board, player)\n \n valid_moves = get_valid_moves(board, player)\n if player == BLACK:\n max_eval = float('-inf')\n for move in valid_moves:\n make_move(board, move, player)\n eval = minimax(board, depth-1, -player)\n max_eval = max(max_eval, eval)\n board[move] = EMPTY\n return max_eval\n else:\n min_eval = float('inf')\n for move in valid_moves:\n make_move(board, move, player)\n eval = minimax(board, depth-1, -player)\n min_eval = min(min_eval, eval)\n board[move] = EMPTY\n return min_eval\n\ndef get_best_move(board, player):\n valid_moves = get_valid_moves(board, player)\n best_move = None\n best_eval = float('-inf') if player == BLACK else float('inf')\n \n for move in valid_moves:\n make_move(board, move, player)\n eval = minimax(board, 3, -player)\n board[move] = EMPTY\n \n if player == BLACK and eval > best_eval:\n best_eval = eval\n best_move = move\n elif player == WHITE and eval < best_eval:\n best_eval = eval\n best_move = move\n \n return best_move\n\n# 示例代码\nplayer = BLACK\nwhile not is_game_over(board):\n if player == BLACK:\n move = get_best_move(board, player)\n make_move(board, move, player)\n player = WHITE\n else:\n # 人类玩家输入坐标\n x, y = map(int, input("请输入落子坐标(x, y): ").split(','))\n move = (x, y)\n if is_valid_move(board, move):\n make_move(board, move, player)\n player = BLACK\n else:\n print("无效的落子，请重新输入！")\n \n print(board)\n\n\n这段代码实现了一个简单的围棋AI，使用了简单的极大极小值算法（Minimax）进行决策。AI和人类玩家交替进行落子，AI通过调用get_best_move函数得到最佳落子位置。minimax函数用于计算当前局面的评估值，evaluate函数用于评估当前棋盘局势。