### 使用Alpha-Beta剪枝算法的五子棋小游戏Python代码 以下是一个完整的五子棋小游戏实现，基于Alpha-Beta剪枝算法，并支持棋盘大小为50x50，AI思考时间限制在1到5秒之间。此外，还将介绍如何将代码打包为可执行文件。 #### 1. Alpha-Beta剪枝算法实现 Alpha-Beta剪枝是一种优化的Minimax算法，用于减少不必要的搜索路径，从而提高效率[^1]。 ```python import copy import random import time # 棋盘大小 SIZE = 50 # 初始化棋盘 def initialize_board(size): return [[0 for _ in range(size)] for _ in range(size)] # 打印棋盘 def print_board(board, size): for i in range(size): for j in range(size): print('X' if board[i][j] == 1 else 'O' if board[i][j] == -1 else '.', end=' ') print() # 检查胜利条件 def check_win(board, player, size): directions = [(1, 0), (0, 1), (1, 1), (1, -1)] for i in range(size): for j in range(size): if board[i][j] == player: for dx, dy in directions: count = 1 for k in range(1, 5): nx, ny = i + dx * k, j + dy * k if 0 <= nx < size and 0 <= ny < size and board[nx][ny] == player: count += 1 else: break if count >= 5: return True return False # 启发式评估函数 def evaluate_position(board, player, opponent, size): score = 0 directions = [(1, 0), (0, 1), (1, 1), (1, -1)] for i in range(size): for j in range(size): if board[i][j] == player: for dx, dy in directions: count = 1 for k in range(1, 5): nx, ny = i + dx * k, j + dy * k if 0 <= nx < size and 0 <= ny < size and board[nx][ny] == player: count += 1 else: break score += count ** 2 elif board[i][j] == opponent: for dx, dy in directions: count = 1 for k in range(1, 5): nx, ny = i + dx * k, j + dy * k if 0 <= nx < size and 0 <= ny < size and board[nx][ny] == opponent: count += 1 else: break score -= count ** 2 return score # Alpha-Beta剪枝算法 def minimax_alpha_beta(board, depth, alpha, beta, maximizing_player, player, opponent, size, start_time, max_time): if depth == 0 or check_win(board, player, size) or check_win(board, opponent, size) or time.time() - start_time > max_time: return evaluate_position(board, player, opponent, size) if maximizing_player: max_eval = float('-inf') for i in range(size): for j in range(size): if board[i][j] == 0: board[i][j] = player eval = minimax_alpha_beta(copy.deepcopy(board), depth - 1, alpha, beta, False, player, opponent, size, start_time, max_time) board[i][j] = 0 max_eval = max(max_eval, eval) alpha = max(alpha, eval) if beta <= alpha: break return max_eval else: min_eval = float('inf') for i in range(size): for j in range(size): if board[i][j] == 0: board[i][j] = opponent eval = minimax_alpha_beta(copy.deepcopy(board), depth - 1, alpha, beta, True, player, opponent, size, start_time, max_time) board[i][j] = 0 min_eval = min(min_eval, eval) beta = min(beta, eval) if beta <= alpha: break return min_eval # AI落子逻辑 def ai_move(board, size, player, opponent, max_time): best_score = float('-inf') move = None start_time = time.time() for i in range(size): for j in range(size): if board[i][j] == 0: board[i][j] = player score = minimax_alpha_beta(copy.deepcopy(board), 3, float('-inf'), float('inf'), False, player, opponent, size, start_time, max_time) board[i][j] = 0 if score > best_score: best_score = score move = (i, j) if time.time() - start_time > max_time: break return move # 主程序 def main(): board = initialize_board(SIZE) player = 1 opponent = -1 turn = 1 while True: if turn == 1: # 玩家回合 print("玩家回合") x, y = map(int, input("请输入你的落子位置（x y）：").split()) if board[x][y] == 0: board[x][y] = player if check_win(board, player, SIZE): print("玩家获胜！") print_board(board, SIZE) break turn = -1 else: print("该位置已被占据，请重新选择。") else: # AI回合 print("AI回合") max_time = random.uniform(1, 5) move = ai_move(board, SIZE, opponent, player, max_time) if move: x, y = move board[x][y] = opponent if check_win(board, opponent, SIZE): print("AI获胜！") print_board(board, SIZE) break turn = 1 print_board(board, SIZE) if __name__ == "__main__": main() ``` #### 2. 将Python代码打包为exe文件 要将上述代码打包为可执行文件，可以使用`pyinstaller`工具[^2]。 ```bash pip install pyinstaller pyinstaller --onefile your_script.py ``` ####