Source code for easyAI.AI.SSS
#contributed by mrfesol (Tomasz Wesolowski)
from easyAI.AI.MTdriver import mtd
[docs]class SSS:
"""
This implements SSS* algorithm. The following example shows
how to setup the AI and play a Connect Four game:
>>> from easyAI import Human_Player, AI_Player, SSS
>>> AI = SSS(7)
>>> game = ConnectFour([AI_Player(AI),Human_Player()])
>>> game.play()
Parameters
-----------
depth:
How many moves in advance should the AI think ?
(2 moves = 1 complete turn)
scoring:
A function f(game)-> score. If no scoring is provided
and the game object has a ``scoring`` method it ill be used.
win_score:
Score LARGER than the largest score of game, but smaller than inf.
It's required to run algorithm.
tt:
A transposition table (a table storing game states and moves)
scoring: can be none if the game that the AI will be given has a
``scoring`` method.
Notes
-----
The score of a given game is given by
>>> scoring(current_game) - 0.01*sign*current_depth
for instance if a lose is -100 points, then losing after 4 moves
will score -99.96 points but losing after 8 moves will be -99.92
points. Thus, the AI will chose the move that leads to defeat in
8 turns, which makes it more difficult for the (human) opponent.
This will not always work if a ``win_score`` argument is provided.
"""
def __init__(self, depth, scoring=None, win_score=100000, tt=None):
self.scoring = scoring
self.depth = depth
self.tt = tt
self.win_score= win_score
def __call__(self,game):
"""
Returns the AI's best move given the current state of the game.
"""
scoring = self.scoring if self.scoring else (
lambda g: g.scoring() ) # horrible hack
first = self.win_score #essence of SSS algorithm
next = (lambda lowerbound, upperbound, bestValue: bestValue)
self.alpha = mtd(game,
first, next,
self.depth,
scoring,
self.tt)
return game.ai_move