Update games.py to third edition.

darius · darius · commit c05e846ce2f5 · 2011-11-06T03:52:31.000Z
diff --git a/games.py b/games.py
@@ -1,8 +1,5 @@
-"""Games, or Adversarial Search. (Chapter 6)
-
+"""Games, or Adversarial Search. (Chapter 5)
 """
-# (Written for the second edition of AIMA; expect some discrepanciecs
-# from the third edition until this gets reviewed.)
 
 from utils import *
 import random
@@ -12,46 +9,44 @@
 
 def minimax_decision(state, game):
     """Given a state in a game, calculate the best move by searching
-    forward all the way to the terminal states. [Fig. 6.4]"""
+    forward all the way to the terminal states. [Fig. 5.3]"""
 
     player = game.to_move(state)
 
     def max_value(state):
         if game.terminal_test(state):
             return game.utility(state, player)
         v = -infinity
-        for (a, s) in game.successors(state):
-            v = max(v, min_value(s))
+        for a in game.actions(state):
+            v = max(v, min_value(game.result(state, a)))
         return v
 
     def min_value(state):
         if game.terminal_test(state):
             return game.utility(state, player)
         v = infinity
-        for (a, s) in game.successors(state):
-            v = min(v, max_value(s))
+        for a in game.actions(state):
+            v = min(v, max_value(game.result(state, a)))
         return v
 
-    # Body of minimax_decision starts here:
-    action, state = argmax(game.successors(state),
-                           lambda ((a, s)): min_value(s))
-    return action
-
+    # Body of minimax_decision:
+    return argmax(game.actions(state),
+                  lambda a: min_value(game.result(state, a)))
 
 #______________________________________________________________________________
 
 def alphabeta_full_search(state, game):
     """Search game to determine best action; use alpha-beta pruning.
-    As in [Fig. 6.7], this version searches all the way to the leaves."""
+    As in [Fig. 5.7], this version searches all the way to the leaves."""
 
     player = game.to_move(state)
 
     def max_value(state, alpha, beta):
         if game.terminal_test(state):
             return game.utility(state, player)
         v = -infinity
-        for (a, s) in game.successors(state):
-            v = max(v, min_value(s, alpha, beta))
+        for a in game.actions(state):
+            v = max(v, min_value(game.result(state, a), alpha, beta))
             if v >= beta:
                 return v
             alpha = max(alpha, v)
@@ -61,17 +56,17 @@ def min_value(state, alpha, beta):
         if game.terminal_test(state):
             return game.utility(state, player)
         v = infinity
-        for (a, s) in game.successors(state):
-            v = min(v, max_value(s, alpha, beta))
+        for a in game.actions(state):
+            v = min(v, max_value(game.result(state, a), alpha, beta))
             if v <= alpha:
                 return v
             beta = min(beta, v)
         return v
 
-    # Body of alphabeta_search starts here:
-    action, state = argmax(game.successors(state),
-                           lambda ((a, s)): min_value(s, -infinity, infinity))
-    return action
+    # Body of alphabeta_search:
+    return argmax(game.actions(state),
+                  lambda a: min_value(game.result(state, a),
+                                      -infinity, infinity))
 
 def alphabeta_search(state, game, d=4, cutoff_test=None, eval_fn=None):
     """Search game to determine best action; use alpha-beta pruning.
@@ -83,8 +78,9 @@ def max_value(state, alpha, beta, depth):
         if cutoff_test(state, depth):
             return eval_fn(state)
         v = -infinity
-        for (a, s) in game.successors(state):
-            v = max(v, min_value(s, alpha, beta, depth+1))
+        for a in game.actions(state):
+            v = max(v, min_value(game.result(state, a),
+                                 alpha, beta, depth+1))
             if v >= beta:
                 return v
             alpha = max(alpha, v)
@@ -94,8 +90,9 @@ def min_value(state, alpha, beta, depth):
         if cutoff_test(state, depth):
             return eval_fn(state)
         v = infinity
-        for (a, s) in game.successors(state):
-            v = min(v, max_value(s, alpha, beta, depth+1))
+        for a in game.actions(state):
+            v = min(v, max_value(game.result(state, a),
+                                 alpha, beta, depth+1))
             if v <= alpha:
                 return v
             beta = min(beta, v)
@@ -106,9 +103,9 @@ def min_value(state, alpha, beta, depth):
     cutoff_test = (cutoff_test or
                    (lambda state,depth: depth>d or game.terminal_test(state)))
     eval_fn = eval_fn or (lambda state: game.utility(state, player))
-    action, state = argmax(game.successors(state),
-                           lambda (a, s): min_value(s, -infinity, infinity, 0))
-    return action
+    return argmax(game.actions(state),
+                  lambda a: min_value(game.result(state, a),
+                                      -infinity, infinity, 0))
 
 #______________________________________________________________________________
 # Players for Games
@@ -120,21 +117,21 @@ def query_player(game, state):
 
 def random_player(game, state):
     "A player that chooses a legal move at random."
-    return random.choice(game.legal_moves(state))
+    return random.choice(game.actions(state))
 
 def alphabeta_player(game, state):
     return alphabeta_search(state, game)
 
 def play_game(game, *players):
     """Play an n-person, move-alternating game.
-    >>> play_game(Fig62Game(), alphabeta_player, alphabeta_player)
+    >>> play_game(Fig52Game(), alphabeta_player, alphabeta_player)
     3
     """
     state = game.initial
     while True:
         for player in players:
             move = player(game, state)
-            state = game.make_move(move, state)
+            state = game.result(state, move)
             if game.terminal_test(state):
                 return game.utility(state, game.to_move(game.initial))
 
@@ -144,17 +141,17 @@ def play_game(game, *players):
 class Game:
     """A game is similar to a problem, but it has a utility for each
     state and a terminal test instead of a path cost and a goal
-    test. To create a game, subclass this class and implement
-    legal_moves, make_move, utility, and terminal_test. You may
-    override display and successors or you can inherit their default
-    methods. You will also need to set the .initial attribute to the
-    initial state; this can be done in the constructor."""
+    test. To create a game, subclass this class and implement actions,
+    result, utility, and terminal_test. You may override display and
+    successors or you can inherit their default methods. You will also
+    need to set the .initial attribute to the initial state; this can
+    be done in the constructor."""
 
-    def legal_moves(self, state):
+    def actions(self, state):
         "Return a list of the allowable moves at this point."
         abstract
 
-    def make_move(self, move, state):
+    def result(self, state, move):
         "Return the state that results from making a move from a state."
         abstract
 
@@ -164,7 +161,7 @@ def utility(self, state, player):
 
     def terminal_test(self, state):
         "Return True if this is a final state for the game."
-        return not self.legal_moves(state)
+        return not self.actions(state)
 
     def to_move(self, state):
         "Return the player whose move it is in this state."
@@ -174,17 +171,12 @@ def display(self, state):
         "Print or otherwise display the state."
         print state
 
-    def successors(self, state):
-        "Return a list of legal (move, state) pairs."
-        return [(move, self.make_move(move, state))
-                for move in self.legal_moves(state)]
-
     def __repr__(self):
         return '<%s>' % self.__class__.__name__
 
-class Fig62Game(Game):
-    """The game represented in [Fig. 6.2]. Serves as a simple test case.
-    >>> g = Fig62Game()
+class Fig52Game(Game):
+    """The game represented in [Fig. 5.2]. Serves as a simple test case.
+    >>> g = Fig52Game()
     >>> minimax_decision('A', g)
     'a1'
     >>> alphabeta_full_search('A', g)
@@ -199,15 +191,12 @@ class Fig62Game(Game):
     utils = Dict(B1=3, B2=12, B3=8, C1=2, C2=4, C3=6, D1=14, D2=5, D3=2)
     initial = 'A'
 
-    def legal_moves(self, state):
-        return [move for (move, next) in self.successors(state)]
+    def actions(self, state):
+        return self.succs.get(state, {}).keys()
 
-    def make_move(self, move, state):
+    def result(self, state, move):
         return self.succs[state][move]
 
-    def successors(self, state):
-        return self.succs.get(state, {}).items()
-
     def utility(self, state, player):
         if player == 'MAX':
             return self.utils[state]
@@ -231,11 +220,11 @@ def __init__(self, h=3, v=3, k=3):
                  for y in range(1, v+1)]
         self.initial = Struct(to_move='X', utility=0, board={}, moves=moves)
 
-    def legal_moves(self, state):
+    def actions(self, state):
         "Legal moves are any square not yet taken."
         return state.moves
 
-    def make_move(self, move, state):
+    def result(self, state, move):
         if move not in state.moves:
             return state # Illegal move has no effect
         board = state.board.copy(); board[move] = state.to_move
@@ -291,12 +280,12 @@ class ConnectFour(TicTacToe):
     def __init__(self, h=7, v=6, k=4):
         TicTacToe.__init__(self, h, v, k)
 
-    def legal_moves(self, state):
+    def actions(self, state):
         return [(x, y) for (x, y) in state.moves
                 if y == 0 or (x, y-1) in state.board]
 
 __doc__ += random_tests("""
->>> play_game(Fig62Game(), random_player, random_player)
+>>> play_game(Fig52Game(), random_player, random_player)
 6
 >>> play_game(TicTacToe(), random_player, random_player)
 0
