diff --git a/AI_map.py b/AI_map.py
new file mode 100644
index 0000000..be7ae59
--- /dev/null
+++ b/AI_map.py
@@ -0,0 +1,345 @@
+import random
+import map_game
+import copy
+import math
+
+class AIMap:
+    def __init__(self,boardMap: list = None):
+        self.map = [[0 for i in range(4)] for i in range(4)] #初始化 全部清零
+        self.map = [[boardMap[i][j]for i in range(4)] for j in range(4)] #复制
+
+    #AI:向左转！
+    def ai_go_left(self):
+        isAdd = 0 # 表示是否成功移动
+        for row in range(0,4):
+            index = 0
+            for col in range(1,4):
+                if self.map[row][col] > 0:
+                    if self.map[row][col] == self.map[row][index]:
+                        self.map[row][index] = self.map[row][col] + self.map[row][index]
+                        self.map[row][col] = 0
+                        index += 1
+                        isAdd = 1
+                    elif self.map[row][index] == 0:
+                        self.map[row][index] = self.map[row][col]
+                        self.map[row][col] = 0
+                        isAdd = 1
+                    else:
+                        index += 1
+
+                    if self.map[row][index] == 0:
+                        self.map[row][index] = self.map[row][col]
+                        self.map[row][col] = 0
+                        isAdd = 1
+        return isAdd
+
+    #AI:向右转！
+    def ai_go_right(self):
+        isAdd = 0 # 表示是否成功移动
+        for row in range(0,4):
+            index = 3
+            for col in range(2,-1,-1):
+                if self.map[row][col] > 0:
+                    if self.map[row][col] == self.map[row][index]:
+                        self.map[row][index] = self.map[row][col] + self.map[row][index]
+                        self.map[row][col] = 0
+                        index -= 1
+                        isAdd = 1
+                    elif self.map[row][index] == 0:
+                        self.map[row][index] = self.map[row][col]
+                        self.map[row][col] = 0
+                        isAdd = 1
+                    else:
+                        index -= 1
+
+                    if self.map[row][index] == 0:
+                        self.map[row][index] = self.map[row][col]
+                        self.map[row][col] = 0
+                        isAdd = 1
+        return isAdd
+
+    #AI:向前进
+    def ai_go_up(self):
+        isAdd = 0 # 表示是否成功移动
+        for col in range(0,4):
+            index = 0
+            for row in range(1,4):
+                if self.map[row][col] > 0:
+                    if self.map[row][col] == self.map[index][col]:
+                        self.map[index][col] = self.map[row][col] + self.map[index][col]
+                        self.map[row][col] = 0
+                        index += 1
+                        isAdd = 1
+                    elif self.map[index][col] == 0:
+                        self.map[index][col] = self.map[row][col]
+                        self.map[row][col] = 0
+                        isAdd = 1
+                    else:
+                        index += 1
+
+                    if self.map[index][col] == 0:
+                        self.map[index][col] = self.map[row][col]
+                        self.map[row][col] = 0
+                        isAdd = 1
+        return isAdd
+
+    #AI:向后退
+    def ai_go_down(self):
+        isAdd = 0 # 表示是否成功移动
+        for col in range(0,4):
+            index = 3
+            for row in range(2,-1,-1):
+                if self.map[row][col] > 0:
+                    if self.map[row][col] == self.map[index][col]:
+                        self.map[index][col] = self.map[row][col] + self.map[index][col]
+                        self.map[row][col] = 0
+                        index -= 1
+                        isAdd = 1
+                    elif self.map[index][col] == 0:
+                        self.map[index][col] = self.map[row][col]
+                        self.map[row][col] = 0
+                        isAdd = 1
+                    else:
+                        index -= 1
+
+                    if self.map[index][col] == 0:
+                        self.map[index][col] = self.map[row][col]
+                        self.map[row][col] = 0
+                        isAdd = 1
+        return isAdd
+
+    #控制AI走不同的方向 走成功为True 走失败为False
+    def ai_go(self,dir):#0左 1右 2上 3下
+        if dir == 0:#左
+            if self.ai_go_left() == 1:
+                return True
+            else:
+                return False
+        elif dir == 1:#右
+            if self.ai_go_right() == 1:
+                return True
+            else:
+                return False
+        elif dir == 2:#上
+            if self.ai_go_up() == 1:
+                return True
+            else:
+                return False
+        elif dir == 3:#下
+            if self.ai_go_down() == 1:
+                return True
+            else:
+                return False
+#数值分析
+#格局评价
+#作为算法的核心，如何评价当前格局的价值是重中之重。
+#在2048中，除了终局外，中间格局并无非常明显的价值评价指标，因此需要用一些启发式的指标来评价格局。
+#那些分数高的好格局是容易引向胜利的格局，而分低的坏格局是容易引向失败的格局。
+    def ai_evaluation(self):
+        #参数需要进行调试
+        #否则会变成人工智障
+        #smoothWeight = 0.5 
+        #monoWeight = 0.03  
+        #emptyWeight = 2.7  
+        #maxWeight = 4
+        #disWeight = 10 
+
+        #人工智障2.0参数
+        smoothWeight = 0.5
+        monoWeight = 0.03
+        emptyWeight = 2.7  
+        maxWeight = 4
+        disWeight = 10 
+
+        result = [disWeight*math.log2(self.dis_weight()), smoothWeight * self.smothness(), monoWeight * self.monotonicity(), 
+                  emptyWeight*math.log2(self.empty_num() + 1), maxWeight*self.max_and_submax_num()]
+        
+        #人工智障3.0参数
+        #smoothWeight = 0.1  # 0.5
+        #monoWeight = 1.0  # 0.03
+        #emptyWeight = 2.7  # 2.7
+        #maxWeight = 1  # 0.01
+        #disWeight = 0
+
+        #smoothWeight = 0.5
+        #monoWeight = 0.03
+        #emptyWeight = 2.7  
+        #maxWeight = 4
+        #disWeight = 10 
+
+        #result = [disWeight*self.dis_weight(), smoothWeight * self.smothness(), monoWeight *
+        #          self.monotonicity(), emptyWeight*math.log(self.empty_num()), maxWeight*self.max_num()]
+   
+        return result
+    #单调性
+    #单调性指方块从左到右、从上到下均遵从递增或递减。
+    ##一般来说，越单调的格局越好。
+    #下面是一个具有良好单调格局的例子
+    #  8    32    64    512
+    #  4     8    16    256
+    #  2     4     8     32
+    #  0     0     4      8
+    def monotonicity(self):
+        totals = [0,0,0,0]  # 四个方向的单调性评估  左 右 上 下（单调递增）
+        #1.横着
+        for i in range(4):
+            current = 0
+            next = current+1
+            while next < 4:
+                while next < 4 and self.map[i][next] == 0:
+                    next += 1
+                if next >= 4:
+                    next -= 1
+
+                if self.map[i][current] != 0: 
+                    currentValue = math.log2(self.map[i][current])
+                else:
+                    currentValue = 0
+
+                if self.map[i][next] != 0: 
+                    nextValue = math.log2(self.map[i][next])
+                else:
+                    nextValue = 0
+
+                if currentValue > nextValue:
+                    totals[0] += nextValue-currentValue
+                else:
+                    totals[1] += currentValue-nextValue
+                current = next
+                next += 1
+        #2.竖着
+        for i in range(4):
+            current = 0
+            next = current+1
+            while next < 4:
+                while next < 4 and self.map[next][i] == 0:
+                    next += 1
+                if next >= 4:
+                    next -= 1
+
+                if self.map[current][i] != 0:
+                    currentValue = math.log2(self.map[current][i])
+                else:
+                    currentValue = 0
+
+                if self.map[next][i] != 0:
+                    nextValue = math.log2(self.map[next][i])
+                else:
+                    nextValue = 0
+
+                if currentValue > nextValue:
+                    totals[2] += nextValue-currentValue
+                else:
+                    totals[3] += currentValue-nextValue
+                current = next
+                next += 1
+        return max(totals[:2])+max(totals[2:])
+
+    #平滑度
+    #平滑性是指每个方块与其直接相邻方块数值的差，其中差越小越平滑。
+    #例如2旁边是4就比2旁边是128平滑。
+    #一般认为越平滑的格局越好。
+    #下面是一个具有极端平滑性的例子
+    #  1024    1024    1024
+    #  1024    1024    1024    1024
+    #  1024    1024    1024    1024
+    #  1024    1024    1024    1024
+    def smothness(self):
+        lubricity = 0
+        for i in range(4):
+            for j in range(4):
+                if self.map[i][j] != 0:
+                    if i >= 1:
+                        # 左边减去该值
+                        lubricity -= abs(math.log2(self.map[i-1][j]+1) - math.log2(self.map[i][j]+1))
+                    if i < 3:
+                        # 右边减去该值
+                        lubricity -= abs(math.log2(self.map[i+1][j]+1) - math.log2(self.map[i][j]+1))
+                    if j > 0:
+                        # 上面减去该值
+                        lubricity -= abs(math.log2(self.map[i][j-1]+1) - math.log2(self.map[i][j]+1))
+                    if j < 3:
+                        # 下面减去该值
+                        lubricity -= abs(math.log2(self.map[i][j+1]+1) - math.log2(self.map[i][j]+1))
+        return lubricity
+
+    #空格数
+    #因为一般来说，空格子越少对玩家越不利。
+    #空格越多的格局越好。
+    def empty_num(self):
+        empty_num = 0
+        for i in range(4):
+            for j in range(4):
+                if self.map[i][j] == 0:
+                    empty_num += 1
+        return empty_num
+
+    #单个最大的值
+    def max_num(self):
+        max_num = 0
+        for i in range(4):
+            for j in range(4):
+                if max_num < self.map[i][j]:
+                    max_num = self.map[i][j]
+        return max_num
+
+    #这个指标评价棋盘的单个数的最大值和总体和
+    #值越大格局越好
+    def max_and_submax_num(self):
+        max_num = 0
+        subMaxNum = 0
+        for row in range(4):
+            for col in range(4):
+                if max_num < self.map[row][col]:
+                    subMaxNum = max_num + subMaxNum;
+                    max_num = self.map[row][col]
+
+        return math.log2(subMaxNum/3+max_num*2/3)  #2.0
+        #return subMaxNum/3+max_num*2/3  #3.0
+
+    #分布位置权重
+    #整体分布越往下，越往右格局越好
+    def dis_weight(self):
+        #wei = [[12, 13, 25, 50],
+        #       [11, 10, 9, 8],
+        #       [4, 5, 6, 7],
+        #       [3, 2, 1, 0]]
+        wei = [[3, 2, 1, 0],
+               [4, 5, 6, 7],
+               [16, 10, 9, 8],
+               [32,64,128,256]]
+        dis_sum = 0
+        for j in range(4):
+            for i in range(4):
+                if self.map[i][j] == 0:  #对数取值不能为0 否则会计算错误
+                    continue
+                dis_sum += math.log2(self.map[i][j])*wei[i][j]  #对数形式
+        return dis_sum
+
+    # 计算分散度，越分散得分越高
+    def islands(self):  
+        islandsMark = 0
+        self.marked = [[True]*4]*4
+        for i in range(4):
+            for j in range(4):
+                if self.map[i][j] != 0:
+                    self.marked[i][j] = False
+        for i in range(4):
+            for j in range(4):
+                if self.map[i][j] != 0 and not self.marked[i][j]:
+                    islandsMark += 1
+                    self.mark(i, j, self.map[i][j])
+        return islandsMark
+
+    def mark(self, x, y, value):
+        # 四个方向向量，方便遍历时使用
+        vectors = [[0, 1], [1, 0], [-1, 0], [0, -1]]
+        if x >= 0 and x <= 3 and y >= 0 and y <= 3 and self.map[x][y] != 0 and self.map[x][y] == value and not self.marked[x][y]:
+            self.marked[x][y] = True
+            for direction in range(4):
+                vector = vectors[direction]
+                self.mark(x+vector[0], y+vector[1], value)
+
+
+
+