def ab_pruning(
board: Board, depth: int, alpha: int, beta: int, max_player: bool) \
-> Tuple[Board, int]:
next_board: Board = None
if depth == 0 or board.victory():
return board, board.evaluate()
elif max_player: # maximizing player
value = -2**32
for child in board.adjacents():
_, node_value = ab_pruning(child, depth - 1, alpha, beta, False)
if node_value > value:
value = node_value
next_board = copy.deepcopy(child)
alpha = max(alpha, value)
if beta <= alpha:
break
return next_board, value
else: # minimizing player
value = 2**32
for child in board.adjacents():
_, node_value = ab_pruning(child, depth - 1, alpha, beta, True)
if node_value < value:
value = node_value
next_board = copy.deepcopy(child)
beta = min(beta, value)
if beta <= alpha:
break
return next_board, value
def ab_pruning(board: Board, depth: int, alpha: int, beta: int,
max_player: bool) -> Tuple[Board, int]:
new_board: Board = None
depth_limit = 5
if depth == 0 or board.victory():
return board, board.evaluate()
elif max_player:
value = -2**32
for child in board.vedle():
k, node_value = ab_pruning(child, depth - 1, alpha, beta, False)
#alpha-beta pruning
if node_value > value:
value = node_value
new_board = copy.deepcopy(child)
alpha = max(alpha, value)
if beta <= alpha:
break
return new_board, value
else:
value = 2**32
for child in board.vedle():
k, node_value = ab_pruning(child, depth - 1, alpha, beta, True)
# alpha-beta pruning
if node_value < value:
value = node_value
new_board = copy.deepcopy(child)
beta = min(beta, value)
if beta <= alpha:
break
return new_board, value
