Artificial Intelligence: A Modern Approach

# AIMA Python file: games.py

```"""Games, or Adversarial Search. (Chapters 6)

"""

from utils import *
import random

# Minimax Search

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]"""

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))
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))
return v

# Body of minimax_decision starts here:
action, state = argmax(game.successors(state),
lambda ((a, s)): min_value(s))
return action

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."""

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))
if v >= beta:
return v
alpha = max(alpha, v)
return v

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))
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

def alphabeta_search(state, game, d=4, cutoff_test=None, eval_fn=None):
"""Search game to determine best action; use alpha-beta pruning.
This version cuts off search and uses an evaluation function."""

player = game.to_move(state)

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))
if v >= beta:
return v
alpha = max(alpha, v)
return v

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))
if v <= alpha:
return v
beta = min(beta, v)
return v

# Body of alphabeta_search starts here:
# The default test cuts off at depth d or at a terminal state
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

# Players for Games

def query_player(game, state):
"Make a move by querying standard input."
game.display(state)

def random_player(game, state):
"A player that chooses a legal move at random."
return random.choice(game.legal_moves())

def alphabeta_player(game, state):
return alphabeta_search(state, game)

def play_game(game, *players):
"Play an n-person, move-alternating game."
state = game.initial
while True:
for player in players:
move = player(game, state)
state = game.make_move(move, state)
if game.terminal_test(state):
return game.utility(state, players)

# Some Sample Games

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."""

def legal_moves(self, state):
"Return a list of the allowable moves at this point."
abstract

def make_move(self, move, state):
"Return the state that results from making a move from a state."
abstract

def utility(self, state, player):
"Return the value of this final state to player."
abstract

def terminal_test(self, state):
"Return True if this is a final state for the game."
return not self.legal_moves(state)

def to_move(self, state):
"Return the player whose move it is in this state."
return state.to_move

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()
>>> minimax_decision('A', g)
'a1'
>>> alphabeta_full_search('A', g)
'a1'
>>> alphabeta_search('A', g)
'a1'
"""
succs = {'A': [('a1', 'B'), ('a2', 'C'), ('a3', 'D')],
'B': [('b1', 'B1'), ('b2', 'B2'), ('b3', 'B3')],
'C': [('c1', 'C1'), ('c2', 'C2'), ('c3', 'C3')],
'D': [('d1', 'D1'), ('d2', 'D2'), ('d3', 'D3')]}
utils = Dict(B1=3, B2=12, B3=8, C1=2, C2=4, C3=6, D1=14, D2=5, D3=2)
initial = 'A'

def successors(self, state):
return self.succs.get(state, [])

def utility(self, state, player):
if player == 'MAX':
return self.utils[state]
else:
return -self.utils[state]

def terminal_test(self, state):
return state not in ('A', 'B', 'C', 'D')

def to_move(self, state):
return if_(state in 'BCD', 'MIN', 'MAX')

class TicTacToe(Game):
"""Play TicTacToe on an h x v board, with Max (first player) playing 'X'.
A state has the player to move, a cached utility, a list of moves in
the form of a list of (x, y) positions, and a board, in the form of
a dict of {(x, y): Player} entries, where Player is 'X' or 'O'."""
def __init__(self, h=3, v=3, k=3):
update(self, h=h, v=v, k=k)
moves = [(x, y) for x in range(1, h+1)
for y in range(1, v+1)]
self.initial = Struct(to_move='X', utility=0, board={}, moves=moves)

def legal_moves(self, state):
"Legal moves are any square not yet taken."
return state.moves

def make_move(self, move, state):
if move not in state.moves:
return state # Illegal move has no effect
board = state.board.copy(); board[move] = state.to_move
moves = list(state.moves); moves.remove(move)
return Struct(to_move=if_(state.to_move == 'X', 'O', 'X'),
utility=self.compute_utility(board, move, state.to_move),
board=board, moves=moves)

def utility(self, state):
"Return the value to X; 1 for win, -1 for loss, 0 otherwise."
return state.utility

def terminal_test(self, state):
"A state is terminal if it is won or there are no empty squares."
return state.utility != 0 or len(state.moves) == 0

def display(self, state):
board = state.board
for x in range(1, self.h+1):
for y in range(1, self.v+1):
print board.get((x, y), '.'),
print

def compute_utility(self, board, move, player):
"If X wins with this move, return 1; if O return -1; else return 0."
if (self.k_in_row(board, move, player, (0, 1)) or
self.k_in_row(board, move, player, (1, 0)) or
self.k_in_row(board, move, player, (1, -1)) or
self.k_in_row(board, move, player, (1, 1))):
return if_(player == 'X', +1, -1)
else:
return 0

def k_in_row(self, board, move, player, (delta_x, delta_y)):
"Return true if there is a line through move on board for player."
x, y = move
n = 0 # n is number of moves in row
while board.get((x, y)) == player:
n += 1
x, y = x + delta_x, y + delta_y
x, y = move
while board.get((x, y)) == player:
n += 1
x, y = x - delta_x, y - delta_y
n -= 1 # Because we counted move itself twice
return n >= self.k

class ConnectFour(TicTacToe):
"""A TicTacToe-like game in which you can only make a move on the bottom
row, or in a square directly above an occupied square.  Traditionally
played on a 7x6 board and requiring 4 in a row."""

def __init__(self, h=7, v=6, k=4):
TicTacToe.__init__(self, h, v, k)

def legal_moves(self, state):
"Legal moves are any square not yet taken."
return [(x, y) for (x, y) in state.moves
if y == 0 or (x, y-1) in state.board]```

 AI: A Modern Approach by Stuart Russell and Peter Norvig Modified: Jul 18, 2005