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alphabeta6.py
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alphabeta6.py
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# Author: Lisa Torrey with modifications by Angelica Munyao
# Purpose: Alpha-beta mini-max agents with depth-limiting
# Citations: Artifical Intelligence Text Book
from framework import Player
from math import inf
class MiniMaxPlayer(Player):
# Initialize the player without an opponent initially
def __init__(self):
self.opponent = None
# Set the player's opponent
def assume(self, opponent):
self.opponent = opponent
# Return whether the player maximizes or not
def maximizes(self):
raise NotImplementedError
# Return the move selected by the player
def move(self, game):
return self.value(game)[1]
# Return the best value of the game for the player
def value(self, game):
raise NotImplementedError
class MaxPlayer(MiniMaxPlayer):
def maximizes(self):
return True
# Return the best value and move for MAX in this game
def value(self, game, alpha=-inf, beta=+inf, depth=0):
# Is the game over?
utility = game.utility()
# Check if we have reached the maximum search depth as per our definition
if utility is None and depth >= 6:
# Use an evaluation function to estimate the outcome of a game
return game.evaluate(self), None
# If the utility is available, return it
if utility is not None:
return utility, None
# Which move leads to the best outcome?
best_value = -inf
best_move = None
for move in game.moves():
child = game.child(move, self)
value = self.opponent.value(child, alpha, beta, depth + 1)[0]
# Maximizing
if best_move is None or value > best_value:
best_value = value
best_move = move
# Pruning
alpha = max(alpha, best_value)
if beta <= alpha:
break
return best_value, best_move
class MinPlayer(MiniMaxPlayer):
def maximizes(self):
return False
# Return the best value and move for MIN in this game
def value(self, game, alpha=-inf, beta=+inf, depth=0):
# Is the game over?
utility = game.utility()
# Check if we have reached the maximum search depth as per our definition
if utility is None and depth >= 6:
# Use an evaluation function to estimate the outcome of a game
return game.evaluate(self), None
# If the utility is available, return it
if utility is not None:
return utility, None
# Which move leads to the best outcome?
best_value = +inf
best_move = None
for move in game.moves():
child = game.child(move, self)
value = self.opponent.value(child, alpha, beta, depth + 1)[0]
# Minimizing
if best_move is None or value < best_value:
best_value = value
best_move = move
# Pruning
beta = min(beta, best_value)
if beta <= alpha:
break
return best_value, best_move