Made AI stronger by working on Minimax/AB pruning

Board.py

@@ -425,6 +425,24 @@ def generate_move_list(self, rdm=True):
 
         return move_list
 
+    def _get_empty_positions(self):
+        return [i.get_index(i.coord) for i in self.points if i.state.name == 'E']
+
+    def _is_closed(self, position):
+
+        for i in self._move_connections[position]:
+            if self.points[i].state.name in {'T', 'E'}:
+                return False
+        return True
+
+    @property
+    def no_of_closed_spaces(self):
+        closed_spaces = 0
+        for i in self._get_empty_positions():
+            if self._is_closed(i):
+                closed_spaces += 1
+        return closed_spaces
+
     def copy(self):
         board = Board()
         board.points = self.points

Engine.py

@@ -21,7 +21,8 @@ def evaluate(self, depth=0):
         """
         winner = self.board.winner
         if not winner:
-            return 30 * self.board.movable_tigers() + 70 * self.board.deadGoats - depth
+            return 300 * self.board.movable_tigers() + 700 * self.board.deadGoats\
+                   - 100 * self.board.no_of_closed_spaces - depth
 
         if winner == Board.Player.G:
             return -Engine.INF
@@ -37,7 +38,7 @@ def minmax(self, is_max=True, depth=0, alpha=-INF, beta=INF):
 
         # find the minimum attainable value for the minimizer
         if not is_max:
-            value = self.INF
+            value = 100000000
             for move in self.board.generate_move_list():
                 # first make the move
                 self.board.make_move(move)
@@ -48,7 +49,7 @@ def minmax(self, is_max=True, depth=0, alpha=-INF, beta=INF):
                 beta = min(beta, value_t)
 
 
-                if value_t <= value:
+                if value_t < value:
                     value = value_t
                     beta = min(beta, value)
                     if depth == 0:
@@ -64,15 +65,15 @@ def minmax(self, is_max=True, depth=0, alpha=-INF, beta=INF):
 
         # find the maximum attainable value for the maximizer
         else:
-            value = -self.INF
+            value = -100000000
             for move in self.board.generate_move_list():
                 # first make the move
                 self.board.make_move(move)
 
                 # go deeper in the search tree recursively
                 value_t = self.minmax(False, depth + 1, alpha, beta)
 
-                if value_t >= value:
+                if value_t > value:
                     value = value_t
                     alpha = max(alpha, value)
                     if depth == 0:
@@ -83,7 +84,7 @@ def minmax(self, is_max=True, depth=0, alpha=-INF, beta=INF):
                 # then revert the move
                 self.board.revert_move(move)
 
-                if alpha > beta:
+                if alpha >= beta:
                     break
 
             return value

ui.py

@@ -28,12 +28,12 @@ def __init__(self, canvas, statustext):
         self.cids = []
         self.from_idx = None
 
-        self.tiger_radius = 7
-        self.goat_radius = 5
+        self.tiger_radius = 20
+        self.goat_radius = 20
 
-        self.board_grid_x = [10, 60, 110, 160, 210]
-        self.board_grid_y = [10, 60, 110, 160, 210]
-        self.board_rect = [1, 1, 221, 221]
+        self.board_grid_x = [30, 130, 230, 330, 430]
+        self.board_grid_y = [30, 130, 230, 330, 430]
+        self.board_rect = [2, 2, 462, 462]
 
         self.game = None
         self.win = ''
@@ -326,10 +326,12 @@ def rules():
 tk = tkinter.Tk()
 tk.title('BaghChal')
 
+tk.resizable(0, 0)
+
 frame = tkinter.Frame(tk)
 frame.pack(fill=BOTH, expand=1)
 
-canvas = tkinter.Canvas(frame, width=220, height=220)
+canvas = tkinter.Canvas(frame, width=460, height=460)
 canvas.pack(fill=BOTH, expand=1, side=TOP, padx=1, pady=1)
 
 statustext = tkinter.StringVar()