def __init__(self, weights, horizon, weights_bis=None, verbose=False):
'''
Constructor
ARGS:
@param weights: Weights for board Static-Evaluation-Function.
@param horizon: Max level for the search algorithm.
@param weights_bis: Weights for the dark-side AI.
'''
self.weights = weights
self.horizon = horizon
self.move = 0
self.board = DBoard()
self.turn = 'LIGHT'
self.gameover = False
self.winner = None
self.nocapturecounter = 0 # Move without a capture.
self.verbose = verbose
if weights_bis is None:
self.weights_bis = self.weights
else:
self.weights_bis = weights_bis
def reset(self):
'''
Reset this brain.
@deprecated: This method can be deleted in future releases.
'''
self.move = 0
self.board = DBoard()
self.turn = 'LIGHT'
self.gameover = False
self.nocapturecounter = 0
def __init__(self, weights, horizon, weights_bis=None, verbose=False):
"""
Constructor
ARGS:
@param weights: Weights for board Static-Evaluation-Function.
@param horizon: Max level for the search algorithm.
@param weights_bis: Weights for the dark-side AI.
"""
self.weights = weights
self.horizon = horizon
self.move = 0
self.board = DBoard()
self.turn = "LIGHT"
self.gameover = False
self.winner = None
self.nocapturecounter = 0 # Move without a capture.
self.verbose = verbose
if weights_bis is None:
self.weights_bis = self.weights
else:
self.weights_bis = weights_bis
self.NUMSLOTS = 2
self.killerMoves = [[] for x in range(self.horizon)]
def __init__(self, weights, horizon, weights_bis=None, verbose=False):
'''
Constructor
ARGS:
@param weights: Weights for board Static-Evaluation-Function.
@param horizon: Max level for the search algorithm.
@param weights_bis: Weights for the dark-side AI.
'''
self.weights = weights
self.horizon = horizon
self.move = 0
self.board = DBoard()
self.turn = 'LIGHT'
self.gameover = False
self.winner = None
self.nocapturecounter = 0 # Move without a capture.
self.verbose = verbose
if weights_bis is None:
self.weights_bis = self.weights
else:
self.weights_bis = weights_bis
def reset(self):
'''
Reset this brain.
@deprecated: This method can be deleted in future releases.
'''
self.move = 0
self.board = DBoard()
self.turn = 'LIGHT'
self.gameover = False
self.nocapturecounter = 0
class DraughtsBrain(object):
'''
Class AI for Draughts.
Use Min-Max with Alpha-Beta Prune.
'''
def __init__(self, weights, horizon, weights_bis=None, verbose=False):
'''
Constructor
ARGS:
@param weights: Weights for board Static-Evaluation-Function.
@param horizon: Max level for the search algorithm.
@param weights_bis: Weights for the dark-side AI.
'''
self.weights = weights
self.horizon = horizon
self.move = 0
self.board = DBoard()
self.turn = 'LIGHT'
self.gameover = False
self.winner = None
self.nocapturecounter = 0 # Move without a capture.
self.verbose = verbose
if weights_bis is None:
self.weights_bis = self.weights
else:
self.weights_bis = weights_bis
def reset(self):
'''
Reset this brain.
@deprecated: This method can be deleted in future releases.
'''
self.move = 0
self.board = DBoard()
self.turn = 'LIGHT'
self.gameover = False
self.nocapturecounter = 0
def switch_turn(self):
'''
Switch current in-game player.
'''
if self.turn == 'LIGHT':
self.turn = 'DARK'
else:
self.turn = 'LIGHT'
def _switch_player(self, player):
'''
Switch player tag.
ARGS:
@param player: Current player.
RETURN:
@return: Next Player.
'''
if player == 'LIGHT':
return 'DARK'
else:
return 'LIGHT'
def run_self(self):
'''
Execute "selfish" AI vs. AI match.
'''
self.gameover = False
while not self.gameover and self.nocapturecounter < 50:
bestmove = self.best_move()
if not bestmove:
self.winner = self._switch_player(self.turn) # No valid move!
break
self.apply_action(bestmove)
if self.verbose:
print(self.board)
print(self.board.board_score(self.weights))
if not self.gameover: # So, too-much noncapture.
self.winner = 'DRAW'
return self.winner
def apply_action(self, action):
'''
Apply an action to board.
ARGS:
@param action: Action that it's going to be executed.
'''
self.board.apply_action(action)
self.move += 1
if len(self.board.light_pieces) == 0:
self.gameover = True
self.winner = 'DARK'
elif len(self.board.dark_pieces) == 0:
self.gameover = True
self.winner = 'LIGHT'
else:
self.switch_turn()
if action.type != 'CAPTURE':
self.nocapturecounter += 1
else:
self.nocapturecounter = 0
########
## AI ##
########
def best_move(self):
'''
Find the next best move according current player state.
This method use the Min-Max algorithm wit Alpha-Beta pruning system
to minimize the number of explored nodes.
RETURN:
@return: One of the best move.
'''
if len(self.board.all_move(self.turn)) == 0:
self.gameover = True
self.winner = self._switch_player(self.turn)
return None
self.path = []
if self.turn == 'LIGHT':
value = self.alphabeta(-float('inf'), float('inf'), self.horizon,
self.turn, self.weights)
else:
value = self.alphabeta(-float('inf'), float('inf'), self.horizon,
self.turn, self.weights_bis)
bestmoves = []
for element in self.path:
if element[
1] == value: # Find path with value equal to best-value.
bestmoves.append(element[0])
else:
if len(bestmoves) == 0 and len(
self.path
) != 0: # If path is not empty return first value.
print("Woops!")
return self.path[0][
0] # WARNING: This code should never be executed.
selected_move = random.choice(
bestmoves) # Select randomly a move among the best ones.
return selected_move
def alphabeta(self, alpha, beta, level, player, weights):
'''
THE GLORIOUS ALPHA-BETA ALGORITHM. GLORIFY HIM.
ARGS:
@param aplha: Current Alpha Value.
@param beta: Current Beta Value.
@param level: Current Level.
@param player: Current Player.
@param weights: Set of weights to use. TODO: Can remove this?
RETURN
'''
if level == 0:
value = self.board.board_score(weights)
self.path.append((self.board.movelist[self.move], value))
return value
if player == 'LIGHT':
moves = self.board.all_move(player)
v = -float('inf')
for mov in moves:
self.board.apply_action(mov)
v = max(
v,
self.alphabeta(alpha, beta, level - 1,
self._switch_player(player), weights))
self.board.undo_last()
if beta <= v:
return v
alpha = max(alpha, v)
if len(moves) == 0:
self.path.append((self.board.movelist[self.move], v))
return v
else:
moves = self.board.all_move(player)
v = float('inf')
for mov in moves:
self.board.apply_action(mov)
v = min(
v,
self.alphabeta(alpha, beta, level - 1,
self._switch_player(player), weights))
self.board.undo_last()
if v <= alpha:
return v
beta = min(beta, v)
if len(moves) == 0:
self.path.append((self.board.movelist[self.move], v))
return v
class DraughtsBrain(object):
'''
Class AI for Draughts.
Use Min-Max with Alpha-Beta Prune.
'''
def __init__(self, weights, horizon, weights_bis=None, verbose=False):
'''
Constructor
ARGS:
@param weights: Weights for board Static-Evaluation-Function.
@param horizon: Max level for the search algorithm.
@param weights_bis: Weights for the dark-side AI.
'''
self.weights = weights
self.horizon = horizon
self.move = 0
self.board = DBoard()
self.turn = 'LIGHT'
self.gameover = False
self.winner = None
self.nocapturecounter = 0 # Move without a capture.
self.verbose = verbose
if weights_bis is None:
self.weights_bis = self.weights
else:
self.weights_bis = weights_bis
def reset(self):
'''
Reset this brain.
@deprecated: This method can be deleted in future releases.
'''
self.move = 0
self.board = DBoard()
self.turn = 'LIGHT'
self.gameover = False
self.nocapturecounter = 0
def switch_turn(self):
'''
Switch current in-game player.
'''
if self.turn == 'LIGHT':
self.turn = 'DARK'
else:
self.turn = 'LIGHT'
def _switch_player(self, player):
'''
Switch player tag.
ARGS:
@param player: Current player.
RETURN:
@return: Next Player.
'''
if player == 'LIGHT':
return 'DARK'
else:
return 'LIGHT'
def run_self(self):
'''
Execute "selfish" AI vs. AI match.
'''
self.gameover = False
while not self.gameover and self.nocapturecounter < 50:
bestmove = self.best_move()
if not bestmove:
self.winner = self._switch_player(self.turn) # No valid move!
break
self.apply_action(bestmove)
if self.verbose:
print(self.board)
print(self.board.board_score(self.weights))
if not self.gameover: # So, too-much noncapture.
self.winner = 'DRAW'
return self.winner
def apply_action(self, action):
'''
Apply an action to board.
ARGS:
@param action: Action that it's going to be executed.
'''
self.board.apply_action(action)
self.move += 1
if len(self.board.light_pieces) == 0:
self.gameover = True
self.winner = 'DARK'
elif len(self.board.dark_pieces) == 0:
self.gameover = True
self.winner = 'LIGHT'
else:
self.switch_turn()
if action.type != 'CAPTURE':
self.nocapturecounter += 1
else:
self.nocapturecounter = 0
########
## AI ##
########
def best_move(self):
'''
Find the next best move according current player state.
This method use the Min-Max algorithm wit Alpha-Beta pruning system
to minimize the number of explored nodes.
RETURN:
@return: One of the best move.
'''
if len(self.board.all_move(self.turn)) == 0:
self.gameover = True
self.winner = self._switch_player(self.turn)
return None
self.path = []
if self.turn == 'LIGHT':
value = self.alphabeta(-float('inf'), float('inf'), self.horizon, self.turn, self.weights)
else:
value = self.alphabeta(-float('inf'), float('inf'), self.horizon, self.turn, self.weights_bis)
bestmoves = []
for element in self.path:
if element[1] == value: # Find path with value equal to best-value.
bestmoves.append(element[0])
else:
if len(bestmoves) == 0 and len(self.path) != 0: # If path is not empty return first value.
print("Woops!")
return self.path[0][0] # WARNING: This code should never be executed.
selected_move = random.choice(bestmoves) # Select randomly a move among the best ones.
return selected_move
def alphabeta(self, alpha, beta, level, player, weights):
'''
THE GLORIOUS ALPHA-BETA ALGORITHM. GLORIFY HIM.
ARGS:
@param aplha: Current Alpha Value.
@param beta: Current Beta Value.
@param level: Current Level.
@param player: Current Player.
@param weights: Set of weights to use. TODO: Can remove this?
RETURN
'''
if level == 0:
value = self.board.board_score(weights)
self.path.append((self.board.movelist[self.move], value))
return value
if player == 'LIGHT':
moves = self.board.all_move(player)
v = -float('inf')
for mov in moves:
self.board.apply_action(mov)
v = max(v, self.alphabeta(alpha, beta, level - 1, self._switch_player(player), weights))
self.board.undo_last()
if beta <= v:
return v
alpha = max(alpha, v)
if len(moves) == 0:
self.path.append((self.board.movelist[self.move], v))
return v
else:
moves = self.board.all_move(player)
v = float('inf')
for mov in moves:
self.board.apply_action(mov)
v = min(v, self.alphabeta(alpha, beta, level - 1, self._switch_player(player), weights))
self.board.undo_last()
if v <= alpha:
return v
beta = min(beta, v)
if len(moves) == 0:
self.path.append((self.board.movelist[self.move], v))
return v
def setUp(self):
self.board = DBoard()
class TestDBoard(unittest.TestCase):
def setUp(self):
self.board = DBoard()
def tearDown(self):
del self.board
def testSetBitMap(self):
'''Check correct behavior of set_bitmap method.'''
self.board.set_bitmap(0, 1, 'DUMMY')
self.assertEqual(self.board.get_piece(0, 1), 'DUMMY',
"DUMMY Isn't where it should...")
def testIsFree(self):
'''Check if is_free recognizes free squares as free squares.'''
self.assert_(self.board.is_free(4, 1))
self.assert_(not self.board.is_free(0, 1))
def testGetPiece(self):
'''Check if get_piece returns right piece.'''
self.board.set_bitmap(0, 1, 'DUMMY')
self.board.set_bitmap(4, 3, 'DUMMY')
self.assertEqual(self.board.get_piece(0, 1), 'DUMMY',
"DUMMY Isn't where it should...")
self.assertEqual(self.board.get_piece(4, 3), 'DUMMY',
"DUMMY Isn't where it should...")
def testApply(self):
'''Check if apply_action applies action in a correct way.'''
piece = self.board.get_piece(0, 1)
action = DAction('MOVE', (0, 1), (4, 1))
self.board.apply_action(action)
self.assertEqual(self.board.get_piece(4, 1), piece,
"Wrong Move Effect.")
def setUp(self):
self.board = DBoard()
class TestDBoard(unittest.TestCase):
def setUp(self):
self.board = DBoard()
def tearDown(self):
del self.board
def testSetBitMap(self):
'''Check correct behavior of set_bitmap method.'''
self.board.set_bitmap(0, 1, 'DUMMY')
self.assertEqual(self.board.get_piece(0, 1), 'DUMMY', "DUMMY Isn't where it should...")
def testIsFree(self):
'''Check if is_free recognizes free squares as free squares.'''
self.assert_(self.board.is_free(4, 1))
self.assert_(not self.board.is_free(0, 1))
def testGetPiece(self):
'''Check if get_piece returns right piece.'''
self.board.set_bitmap(0, 1, 'DUMMY')
self.board.set_bitmap(4, 3, 'DUMMY')
self.assertEqual(self.board.get_piece(0, 1), 'DUMMY', "DUMMY Isn't where it should...")
self.assertEqual(self.board.get_piece(4, 3), 'DUMMY', "DUMMY Isn't where it should...")
def testApply(self):
'''Check if apply_action applies action in a correct way.'''
piece = self.board.get_piece(0, 1)
action = DAction('MOVE', (0, 1), (4, 1))
self.board.apply_action(action)
self.assertEqual(self.board.get_piece(4, 1), piece, "Wrong Move Effect.")
