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五子棋作为古老的策略游戏,其AI设计完美融合了图论与模式识别技术。本文将深入解析胜利条件判断的拓扑学原理、蒙特卡洛树搜索的优化策略以及神经网络评估函数的设计哲学,揭示人机对弈背后的计算智能本质。
采用稀疏矩阵编码棋盘:
B i j = { + 1 黑子 − 1 白子 0 空位 B_{ij} = \begin{cases} +1 & \text{黑子} \\ -1 & \text{白子} \\ 0 & \text{空位} \end{cases} Bij=⎩ ⎨ ⎧+1−10黑子白子空位
Zobrist哈希算法:
h a s h n e w = h a s h o l d ⊕ Z [ i ] [ j ] [ p l a y e r ] hash_{new} = hash_{old} \oplus Z[i][j][player] hashnew=hashold⊕Z[i][j][player]
其中 Z Z Z为预生成的随机数表
Start[落子位置] --> Check[8方向扫描]
Check --> Count[连续计数]
Count --> Judge{≥5?}
Judge -->|是| Win[胜利]
Judge -->|否| Continue[继续]
预定义胜利模板:
[ 1 1 1 1 1 ] [ 1 1 1 1 1 ] [ 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 ] \begin{bmatrix} 1 & 1 & 1 & 1 & 1 \end{bmatrix} \quad \begin{bmatrix} 1 \\ 1 \\ 1 \\ 1 \\ 1 \end{bmatrix} \quad \begin{bmatrix} 1 & 0 & 0 & 0 & 0 \\ 0 & 1 & 0 & 0 & 0 \\ 0 & 0 & 1 & 0 & 0 \\ 0 & 0 & 0 & 1 & 0 \\ 0 & 0 & 0 & 0 & 1 \end{bmatrix} [11111] 11111 1000001000001000001000001
评估函数递推:
V ( s ) = { 效用值 终止状态 max a ∈ A V ( s ′ ) MAX层 min a ∈ A V ( s ′ ) MIN层 V(s) = \begin{cases} \text{效用值} & \text{终止状态} \\ \max_{a \in A} V(s') & \text{MAX层} \\ \min_{a \in A} V(s') & \text{MIN层} \end{cases} V(s)=⎩ ⎨ ⎧效用值maxa∈AV(s′)mina∈AV(s′)终止状态MAX层MIN层
剪枝条件:
α ≥ β ⇒ 终止分支 \alpha \geq \beta \Rightarrow \text{终止分支} α≥β⇒终止分支
节点选择策略:
U C T ( v i ) = Q ( v i ) N ( v i ) + c ln N ( v p a r e n t ) N ( v i ) UCT(v_i) = \frac{Q(v_i)}{N(v_i)} + c\sqrt{\frac{\ln N(v_{parent})}{N(v_i)}} UCT(vi)=N(vi)Q(vi)+cN(vi)lnN(vparent)
双残差网络架构:
存储关键信息:
E n t r y = { h a s h 棋盘指纹 d e p t h 搜索深度 f l a g 类型标记 v a l u e 评估值 Entry = \begin{cases} hash & \text{棋盘指纹} \\ depth & \text{搜索深度} \\ flag & \text{类型标记} \\ value & \text{评估值} \end{cases} Entry=⎩ ⎨ ⎧hashdepthflagvalue棋盘指纹搜索深度类型标记评估值
任务划分策略:
T i = { 节点 j ∣ j m o d N = i } T_i = \{\text{节点}_j | j \mod N = i\} Ti={节点j∣jmodN=i}
参数动态调整:
{ S e a r c h D e p t h = 3 + 2 × L e v e l S i m u l a t i o n s = 100 × 2 L e v e l \begin{cases} SearchDepth = 3 + 2 \times Level \\ Simulations = 100 \times 2^{Level} \end{cases} {SearchDepth=3+2×LevelSimulations=100×2Level
热力图可视化:
H e a t i j = exp ( Q ( s , a i j ) ) ∑ exp ( Q ( s , a ) ) Heat_{ij} = \frac{\exp(Q(s,a_{ij}))}{\sum \exp(Q(s,a))} Heatij=∑exp(Q(s,a))exp(Q(s,aij))
五子棋AI的设计展现了古典博弈论与现代深度学习的完美结合。从精确的胜利条件判断到智能的落子策略生成,每个技术模块都在重新定义人机对抗的认知边界。这种范式为回合制策略游戏AI开发提供了标准范式。
延伸应用场景:
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
游戏核心逻辑模块
"""
import pygame
import numpy as np
from effects import SoundManager, AnimationManager
class Game:
def __init__(self):
# 游戏参数设置
self.board_size = 15 # 15x15的棋盘
self.cell_size = 40 # 每个格子的大小
self.grid_size = self.board_size * self.cell_size # 棋盘网格总大小
# 界面尺寸设置
self.margin = 50 # 棋盘边距
self.info_width = 300 # 信息栏宽度
self.width = self.grid_size + 2 * self.margin + self.info_width
self.height = self.grid_size + 2 * self.margin
# 创建游戏窗口
self.screen = pygame.display.set_mode((self.width, self.height))
# 加载资源
self.background = pygame.Surface((self.width, self.height))
self.background.fill((240, 217, 181)) # 木色背景
# 初始化棋盘状态
self.board = np.zeros((self.board_size, self.board_size), dtype=int)
# 记录最后一步落子
self.last_move = None
# 游戏状态
self.current_player = 1 # 1代表黑棋(人类),-1代表白棋(AI)
self.game_over = False
self.winner = None
# 用于显示热力图
self.heatmap = None
# 初始化音效系统
self.sound_manager = SoundManager()
# 初始化动画系统
self.animation_manager = AnimationManager(self.screen)
# 胜利线段坐标
self.win_line = None
def get_board_position(self, x, y):
"""将屏幕坐标转换为棋盘位置"""
# 计算相对于棋盘左上角的偏移
board_x = x - self.margin
board_y = y - self.margin
# 计算最近的交叉点
row = round(board_y / self.cell_size)
col = round(board_x / self.cell_size)
# 确保在棋盘范围内
if 0 <= row < self.board_size and 0 <= col < self.board_size:
return row, col
return None, None
def is_valid_move(self, row, col):
"""检查是否可以在指定位置落子"""
if row is None or col is None:
return False
return 0 <= row < self.board_size and 0 <= col < self.board_size and self.board[row][col] == 0
def make_move(self, row, col):
"""在指定位置落子"""
if self.is_valid_move(row, col):
self.board[row][col] = self.current_player
self.last_move = (row, col)
# 添加落子动画
x = self.margin + col * self.cell_size
y = self.margin + row * self.cell_size
color = (0, 0, 0) if self.current_player == 1 else (255, 255, 255)
self.animation_manager.add_stone_animation(x, y, color, self.cell_size // 2 - 2)
# 播放落子音效
self.sound_manager.play("stone")
# 切换玩家
self.current_player = -self.current_player
return True
return False
def check_win(self, row, col):
"""检查是否有玩家获胜(按照第二章的胜利条件判断算法)"""
player = self.board[row][col]
directions = [(0, 1), (1, 0), (1, 1), (1, -1)] # 水平、垂直、两条对角线
for dr, dc in directions:
count = 1 # 当前位置已经有一个子
# 保存连续棋子的位置
positions = [(row, col)]
# 正向检查
r, c = row + dr, col + dc
while 0 <= r < self.board_size and 0 <= c < self.board_size and self.board[r][c] == player:
count += 1
positions.append((r, c))
r += dr
c += dc
# 反向检查
r, c = row - dr, col - dc
while 0 <= r < self.board_size and 0 <= c < self.board_size and self.board[r][c] == player:
count += 1
positions.insert(0, (r, c))
r -= dr
c -= dc
# 判断是否达到5子连珠
if count >= 5:
# 保存胜利线的起点和终点
start_row, start_col = positions[0]
end_row, end_col = positions[-1]
# 添加胜利线动画
start_x = self.margin + start_col * self.cell_size
start_y = self.margin + start_row * self.cell_size
end_x = self.margin + end_col * self.cell_size
end_y = self.margin + end_row * self.cell_size
color = (255, 0, 0) # 红色胜利线
self.animation_manager.add_win_line_animation(start_x, start_y, end_x, end_y, color)
# 保存胜利线信息
self.win_line = (start_x, start_y, end_x, end_y)
# 播放胜利/失败音效
if player == 1: # 人类玩家
self.sound_manager.play("win")
else: # AI玩家
self.sound_manager.play("lose")
return True
return False
def draw(self):
"""绘制游戏界面"""
# 绘制背景
self.screen.blit(self.background, (0, 0))
# 绘制棋盘网格
for i in range(self.board_size):
# 横线
pygame.draw.line(
self.screen,
(0, 0, 0),
(self.margin, self.margin + i * self.cell_size),
(self.margin + (self.board_size - 1) * self.cell_size, self.margin + i * self.cell_size),
2 if i == 0 or i == self.board_size - 1 else 1
)
# 竖线
pygame.draw.line(
self.screen,
(0, 0, 0),
(self.margin + i * self.cell_size, self.margin),
(self.margin + i * self.cell_size, self.margin + (self.board_size - 1) * self.cell_size),
2 if i == 0 or i == self.board_size - 1 else 1
)
# 绘制天元和星位
star_points = [(3, 3), (3, 11), (7, 7), (11, 3), (11, 11)]
for row, col in star_points:
pygame.draw.circle(
self.screen,
(0, 0, 0),
(self.margin + col * self.cell_size, self.margin + row * self.cell_size),
5
)
# 绘制热力图(如果有)
if self.heatmap is not None and not self.game_over:
max_value = np.max(self.heatmap) if np.max(self.heatmap) > 0 else 1
for row in range(self.board_size):
for col in range(self.board_size):
if self.board[row][col] == 0 and self.heatmap[row][col] > 0:
# 计算透明度,根据落子价值归一化
alpha = int(min(255, self.heatmap[row][col] / max_value * 200))
s = pygame.Surface((self.cell_size - 4, self.cell_size - 4), pygame.SRCALPHA)
s.fill((255, 0, 0, alpha))
self.screen.blit(
s,
(
self.margin + col * self.cell_size - (self.cell_size - 4) // 2,
self.margin + row * self.cell_size - (self.cell_size - 4) // 2
)
)
# 绘制棋子 - 无论动画是否在播放,都需要绘制棋子
for row in range(self.board_size):
for col in range(self.board_size):
if self.board[row][col] != 0:
color = (0, 0, 0) if self.board[row][col] == 1 else (255, 255, 255)
pygame.draw.circle(
self.screen,
color,
(self.margin + col * self.cell_size, self.margin + row * self.cell_size),
self.cell_size // 2 - 2
)
# 为白棋添加黑色边框
if self.board[row][col] == -1:
pygame.draw.circle(
self.screen,
(0, 0, 0),
(self.margin + col * self.cell_size, self.margin + row * self.cell_size),
self.cell_size // 2 - 2,
2
)
# 标记最后一步棋
if self.last_move:
row, col = self.last_move
pygame.draw.rect(
self.screen,
(255, 0, 0),
(
self.margin + col * self.cell_size - 5,
self.margin + row * self.cell_size - 5,
10,
10
),
2
)
# 绘制胜利线(如果有)
if self.win_line and self.game_over:
start_x, start_y, end_x, end_y = self.win_line
pygame.draw.line(
self.screen,
(255, 0, 0),
(start_x, start_y),
(end_x, end_y),
5
)
# 绘制信息栏
info_x = self.grid_size + 2 * self.margin
pygame.draw.line(
self.screen,
(0, 0, 0),
(info_x, 0),
(info_x, self.height),
2
)
# 创建字体
font = pygame.font.SysFont('SimHei', 24)
small_font = pygame.font.SysFont('SimHei', 18)
# 显示当前玩家
current_text = "当前玩家: 黑棋(玩家)" if self.current_player == 1 else "当前玩家: 白棋(AI)"
current_surface = font.render(current_text, True, (0, 0, 0))
self.screen.blit(current_surface, (info_x + 20, 50))
# 显示游戏状态
if self.game_over:
if self.winner == 1:
result_text = "游戏结束: 黑棋(玩家)胜利!"
elif self.winner == -1:
result_text = "游戏结束: 白棋(AI)胜利!"
else:
result_text = "游戏结束: 平局!"
result_surface = font.render(result_text, True, (255, 0, 0))
self.screen.blit(result_surface, (info_x + 20, 100))
# 添加重新开始游戏的提示
restart_surface = small_font.render("按下 R 键重新开始游戏", True, (0, 0, 0))
self.screen.blit(restart_surface, (info_x + 20, 150))
# 显示游戏说明
help_texts = [
"游戏规则:",
"- 黑方先行",
"- 五子连珠获胜",
"- 点击棋盘落子",
"",
"操作说明:",
"- H 键: 显示AI落子热力图",
"- R 键: 重新开始游戏",
"- 1-5 键: 调整AI难度",
"- S 键: 切换音效开关"
]
for i, text in enumerate(help_texts):
help_surface = small_font.render(text, True, (0, 0, 0))
self.screen.blit(help_surface, (info_x + 20, 200 + i * 30))
# 绘制音效状态
sound_text = f"音效: {'开启' if self.sound_manager.enabled else '关闭'}"
sound_surface = small_font.render(sound_text, True, (0, 0, 0))
self.screen.blit(sound_surface, (info_x + 20, 530))
# 执行动画更新 - 注意:这只应该更新动画,不应该绘制棋子
self.animation_manager.update()
def reset(self):
"""重置游戏"""
self.board = np.zeros((self.board_size, self.board_size), dtype=int)
self.last_move = None
self.current_player = 1
self.game_over = False
self.winner = None
self.heatmap = None
self.win_line = None