代码参考自中国大学mooc上人工智能与信息社会陈斌老师的算法，我在原来的基础上增加了玩家输入的异常捕获 AlphaBeta剪枝算法是对Minimax方法的优化，能够极大提高搜索树的效率，如果对这个算法感兴趣的可以去参考相关资料。 当正确理解AlphaBeta剪枝算法后，还可以将它应用在象棋、围棋等一些高级游戏的算法搜索上，使得电脑寻找最优胜率的速度加快
python代码实现
#coding:utf-8
'''井字棋(Tic tac toe)Python3语言实现, 带有Alpha-Beta剪枝的Minimax算法.
代码参考自中国大学mooc 人工智能与信息社会(陈斌)'''
import random
# 用如下的9个数字来表示棋盘的位置:
# 0 1 2
# 3 4 5
# 6 7 8
# 设定获胜的组合方式(横、竖、斜)
WINNING_TRIADS = ((0, 1, 2), (3, 4, 5), (6, 7, 8),
(0, 3, 6), (1, 4, 7),(2, 5, 8),
(0, 4, 8), (2, 4, 6))
# 设定棋盘按一行三个打印
PRINTING_TRIADS = ((0, 1, 2), (3, 4, 5), (6, 7, 8))
# 用一维列表表示棋盘:
SLOTS = (0, 1, 2, 3, 4, 5, 6, 7, 8)
# -1表示X玩家 0表示空位 1表示O玩家.
X_token = -1
Open_token = 0
O_token = 1
MARKERS = ['_', 'O', 'X']
END_PHRASE = ('平局', '胜利', '失败')
def alpha_beta_valuation(board, player, next_player, alpha, beta):
"""运用AlphaBeta剪枝来计算当前局面的分值
因为搜索层数少，总能搜索到最终局面，估值结果为[-1,0,1]
"""
wnnr = winner(board)
if wnnr != Open_token:
# 有玩家获胜
return wnnr
elif not legal_move_left(board):
# 没有空位,平局
return 0
# 检查当前玩家"player"的所有可落子点
for move in SLOTS:
if board[move] == Open_token:
board[move] = player
# 落子之后交换玩家，继续检验
val = alpha_beta_valuation(board, next_player, player, alpha, beta)
board[move] = Open_token
if player == O_token: # 当前玩家是O,是Max玩家(记号是1)
if val > alpha:
alpha = val
if alpha >= beta:
return beta # 直接返回当前的最大可能取值beta, 进行剪枝
else: # 当前玩家是X,是Min玩家(记号是-1)
if val < beta:
beta = val
if beta <= alpha:
return alpha # 直接返回当前的最小可能取值alpha, 进行剪枝
if player == O_token:
retval = alpha
else:
retval = beta
return retval
def print_board(board):
"""打印当前棋盘"""
for row in PRINTING_TRIADS:
r = ' '
for hole in row:
r += MARKERS[board[hole]] + ' '
print(r)
def legal_move_left(board):
""" 判断棋盘上是否还有空位 """
for slot in SLOTS:
if board[slot] == Open_token:
return True
return False
def winner(board):
""" 判断局面的胜者,返回值-1表示X获胜,1表示O获胜,0表示平局或者未结束"""
for triad in WINNING_TRIADS:
triad_sum = board[triad[0]] + board[triad[1]] + board[triad[2]]
if triad_sum == 3 or triad_sum == -3:
return board[triad[0]] # 表示棋子的数值恰好也是-1:X,1:O
return 0
def determine_move(board):
"""决定电脑(玩家O)的下一步棋,若估值相同则随机选取步数"""
best_val = -2 # 本程序估值结果只在[-1,0,1]中
my_moves = []
print("开始思考")
for move in SLOTS:
if board[move] == Open_token:
board[move] = O_token
val = alpha_beta_valuation(board, X_token, O_token, -2, 2)
board[move] = Open_token
print("Computer如果下在", move, ",将导致", END_PHRASE[val])
if val > best_val:
best_val = val
my_moves = [move]
if val == best_val:
my_moves.append(move)
return random.choice(my_moves)
HUMAN = 1
COMPUTER = 0
def main():
"""主函数,先决定谁是X(先手方),再开始下棋"""
next_move = HUMAN
opt = input("请选择先手方，输入X表示玩家先手，输入O表示电脑先手：")
if opt == "X":
next_move = HUMAN
elif opt == "O":
next_move = COMPUTER
else:
print("输入有误，默认玩家先手")
# 初始化空棋盘
board = [Open_token for i in range(9)]
# 开始下棋
while legal_move_left(board) and winner(board) == Open_token:
print()
print_board(board)
if next_move == HUMAN and legal_move_left(board):
try:
humanmv = int(input("请输入你要落子的位置(0-8)："))
if board[humanmv] != Open_token:
continue
board[humanmv] = X_token
next_move = COMPUTER
except:
print("输入有误，请重试")
continue
if next_move == COMPUTER and legal_move_left(board):
mymv = determine_move(board)
print("Computer最终决定下在", mymv)
board[mymv] = O_token
next_move = HUMAN
# 输出结果
print_board(board)
print(["平局", "Computer赢了", "你赢了"][winner(board)])
if __name__ == '__main__':
main()
运行结果
)
