def paranoid(node, searching_player, alpha=-sys.maxsize, beta=sys.maxsize):
# If the max depth or goal state has been reached.
if node.depth == DIFFICULTY or not any(node.board[BOARD_SIZE*node.turn+1:(node.turn+1)*BOARD_SIZE-1]):
return assign_paranoid_value(node.board, searching_player), None
global NODES_VISITED
best_move = None
searcher = node.turn == searching_player
v = sys.maxsize*(-1 if searcher else 1)
for m in POSSIBLE_MOVES:
result = do_move(m, node.turn, deepcopy(node.board))
if result == None: continue
val, _ = paranoid(Node(result, node.depth+1), searching_player, alpha, beta)
NODES_VISITED += 1
if searcher:
if val > alpha: best_move = m
v = max(v, val)
alpha = max(alpha, v)
else:
if val < beta: best_move = m
v = min(v, val)
beta = min(beta, v)
if beta <= alpha: break
return v, best_move
def max_n(node, searching_player):
# If the max depth or goal state has been reached.
if node.depth == DIFFICULTY or not any(node.board[BOARD_SIZE*node.turn+1:(node.turn+1)*BOARD_SIZE-1]):
return [assign_maxn_value(node.board, p) for p in PLAYERS], None
global NODES_VISITED
best_move = None
best_result = [sys.maxsize*(-1)]*NUM_PLAYERS
for m in POSSIBLE_MOVES:
result = do_move(m, node.turn, deepcopy(node.board))
if result == None: continue
values, _ = max_n(Node(result, node.depth+1), searching_player)
NODES_VISITED += 1
if values[searching_player.row] > best_result[searching_player.row]:
best_result = values; best_move = m
return best_result, best_move
def min_max(node, searching_player):
# If the max depth or goal state has been reached.
if node.depth == DIFFICULTY or not any(node.board[BOARD_SIZE*node.turn+1:(node.turn+1)*BOARD_SIZE-1]):
return assign_value(node.board, searching_player), None
global NODES_VISITED
best_move = None
searcher = node.turn == searching_player
v = sys.maxsize*(-1 if searcher else 1)
for m in POSSIBLE_MOVES:
result = do_move(m, node.turn, deepcopy(node.board))
if result == None: continue
val, _ = min_max(Node(result, node.depth+1), searching_player)
NODES_VISITED += 1
if searcher and val > v or not searcher and val < v:
v = val; best_move = m
return v, best_move
def cooperative(node, searching_player):
b, row_size = node.board, BOARD_SIZE
# If the max depth or goal state has been reached.
if node.depth == DIFFICULTY or not any(node.board[BOARD_SIZE*node.turn+1:(node.turn+1)*BOARD_SIZE-1]):
return [assign_maxn_value(node.board, p) for p in PLAYERS], None
global NODES_VISITED
best_move = None
best_result = [sys.maxsize*(-1)]*NUM_PLAYERS
best_value = sys.maxsize*(-1)
for m in POSSIBLE_MOVES:
result = do_move(m, node.turn, deepcopy(node.board))
if result == None: continue
values, _ = cooperative(Node(result, node.depth+1), searching_player)
NODES_VISITED += 1
this_val = values[searching_player.row]*2 + values[PREVIOUS_PLAYER.row]
if this_val > best_value:
best_value, best_result, best_move = this_val, values, m
return best_result, best_move
