def area_descriptor(area: Area, board: Board):
neighborhood = area.get_adjacent_areas()
player_name = area.get_owner_name()
enemy_areas = [
adj for adj in neighborhood
if board.get_area(adj).get_owner_name() != player_name
]
owned_areas = [
adj for adj in neighborhood
if board.get_area(adj).get_owner_name() == player_name
]
unique_enemies = {
board.get_area(adj).get_owner_name()
for adj in neighborhood
}
if player_name in unique_enemies:
unique_enemies.remove(player_name)
max_region_size = max(
len(r) for r in board.get_players_regions(player_name))
feature_vector = [
survival_prob(area, board),
rel_area_power(area, board),
len(enemy_areas),
len(owned_areas),
len(unique_enemies), max_region_size / len(board.areas)
]
return np.asarray(feature_vector, dtype=np.float32)
def _heuristic(self, players: List[int], board: Board) -> Dict[int, float]:
"""
Computes the heuristic function for all the given players.
:param players: Names of players.
:param board: The game board.
:return: A dictionary where keys are names of given players and values
are values of the heuristic function for these players in a
current game board.
"""
h = {}
for player in players:
h[player] = board.get_player_dice(player)
players_regions = board.get_players_regions(player)
players_regions_sizes = []
for region in players_regions:
region_size = len(region)
h[player] += self.__REG_HEURISTIC_WEIGHT * region_size
players_regions_sizes.append(region_size)
h[player] += \
self.__LARGEST_REG_HEURISTIC_WEIGHT * max(players_regions_sizes)
return h
def possible_attacks(board: Board, player_name: int):
for source in board.get_player_border(player_name):
if not source.can_attack():
continue
for adj in source.get_adjacent_areas():
target = board.get_area(adj)
if target.get_owner_name() != player_name:
succ_prob = ATTACK_SUCC_PROBS[source.get_dice()][
target.get_dice()]
yield source, target, succ_prob
def possible_attacks(board: Board,
player_name: int) -> Iterator[Tuple[int, int]]:
for area in board.get_player_border(player_name):
if not area.can_attack():
continue
neighbours = area.get_adjacent_areas()
for adj in neighbours:
adjacent_area = board.get_area(adj)
if adjacent_area.get_owner_name() != player_name:
yield (area, adjacent_area)
def path_heuristics(board: Board, path: list) -> float:
"""
Heuristika = součet všech dílčích heuristik v cestě
"""
if len(path) < 2:
return 0
with simulate_battle(board.get_area(path[0]), board.get_area(path[1])):
h = path_heuristics(board, path[1:])
h += battle_heuristic(board, board.get_area(path[0]), board.get_area(path[1]))
return h
def game_descriptor(board: Board, player_name: int, players: list):
areas = board.get_player_areas(player_name)
regions = board.get_players_regions(player_name)
border = board.get_player_border(player_name)
max_region_size = max(len(r) for r in regions)
rel_border_size_1 = len(border) / sum(
len(board.get_player_border(name)) for name in players)
rel_border_size_2 = sum(a.get_dice() for a in border) / sum(a.get_dice()
for a in areas)
rel_area_size = len(areas) / sum(
len(board.get_player_areas(name)) for name in players)
best_border = []
for r in regions:
if len(r) == max_region_size:
for area in map(lambda a: board.get_area(a), r):
if board.is_at_border(area):
best_border.append(area)
feature_vector = [
max_region_size / len(board.areas), rel_border_size_1,
rel_border_size_2, rel_area_size,
np.mean([survival_prob(a, board) for a in best_border]),
np.mean([rel_area_power(a, board) for a in areas])
]
return np.asarray(feature_vector, dtype=np.float32)
def get_attackable(board: Board, active_area: Area):
neighbors = active_area.get_adjacent_areas()
attackable = []
for nb in neighbors:
a = board.get_area(nb)
if active_area.get_owner_name() != a.get_owner_name():
attackable.append(a)
return attackable
def survival_prob(target: Area, board: Board):
prob = 1.
for adj in target.get_adjacent_areas():
source = board.get_area(adj)
if source.get_owner_name() == target.get_owner_name(
) or not source.can_attack():
continue
prob *= 1. - ATTACK_SUCC_PROBS[source.get_dice()][target.get_dice()]
return prob
def rel_area_power(area: Area, board: Board):
player_power = area.get_dice()
total_power = player_power
for adj in area.get_adjacent_areas():
adj_area = board.get_area(adj)
dice = adj_area.get_dice()
if adj_area.get_owner_name() == area.get_owner_name():
player_power += dice
total_power += dice
return player_power / total_power
def add_dice_to_player(board: Board, player_name: int):
"""Vytvoří dočasné herní pole s náhodně přidělenými kostkami pro vybraného
hráče. Základní logika předělování kostek je převzatá z implemenetace hry
viz dicewars/server/game.py line:230.
Funkce se používá stejně jako funkce simulate_battle.
Args
----
board (Board): Aktuální stav herního pole
player_name (int): Jméno hráče, kterému se mají přiřadit kostky
"""
affected_areas: Dict[int, int] = {}
dice = 0
regions = board.get_players_regions(player_name)
for region in regions:
dice = max(dice, len(region))
areas: List[Area] = []
for area in board.get_player_areas(player_name):
areas.append(area)
if dice > 64:
dice = 64
while dice and areas:
area = random.choice(areas)
if area.dice >= 8:
areas.remove(area)
else:
if area.name not in affected_areas:
affected_areas[area.name] = area.dice
area.dice += 1
dice -= 1
try:
yield
finally:
# Vrátit herní pole do původního stavu
for name in affected_areas:
board.get_area(name).dice = affected_areas[name]
def __largest_region(player_name: int, board: Board) -> List[int]:
"""
Returns the largest region of a given player.
:param player_name: A name (ID) of the associated player.
:param board: The game board.
:return: The largest region of a given player. (A list of area
names (IDs) in the largest region.)
"""
players_regions = board.get_players_regions(player_name)
max_region_size = max(len(r) for r in players_regions)
return [r for r in players_regions if len(r) == max_region_size][0]
def __perform_atack(board: Board, a_name: int, b_name: int) -> None:
"""
Performes a successful atack.
:param board: The game board.
:param a_name: A name of an atacker area.
:param b_name: A name of a defender area.
"""
a = board.get_area(a_name)
b = board.get_area(b_name)
b.set_dice(a.get_dice() - 1)
a.set_dice(1)
b.set_owner(a_name)
def player_heuristic(board: Board, player_name: int) -> int:
"""Výpočet heuristiky pro hráče podle aktuálního stavu herní plochy.
koeficient = počet_polí_hlavního_území
Args
----
board (Board): Aktuální stav herní plochy
player_name (int): Jméno hráče
Returns
-------
int: Výsledná hodnota heuristiky. Větší znamená lepší
"""
score = 0
regions = board.get_players_regions(player_name)
for region in regions:
score = max(score, len(region))
return score
def __possible_turns(self, player_name: int,
board: Board) -> List[Tuple[int, int]]:
"""
Returns possible turns with higher hold probability than a trashold.
(The single turn expectiminimax.)
:param player_name: A name (ID) of the associated player.
:param board: The game board.
:return: Possible turns with higher hold probability than a trashold.
(A list of tuples (atacker area; defender area) sorted by
a weight in descending order.)
"""
if board.nb_players_alive() > self.__ATK_PLAYERS_LIMIT_2:
atk_prob_treshold = self.__ATK_PROB_TRESHOLD_2
atk_from_larges_reg_weight = self.__ATK_FROM_LARGEST_REG_WEIGHT_2
else:
atk_prob_treshold = self.__ATK_PROB_TRESHOLD
atk_from_larges_reg_weight = self.__ATK_FROM_LARGEST_REG_WEIGHT
turns = []
largest_region = self.__largest_region(player_name, board)
for a, b in possible_attacks(board, player_name):
a_name = a.get_name()
b_name = b.get_name()
atk_power = a.get_dice()
p = probability_of_successful_attack(board, a_name, b_name)
p *= probability_of_holding_area(board, b_name, atk_power - 1,
player_name)
if p >= atk_prob_treshold or atk_power == self.__MAX_DICE:
if a_name in largest_region:
p *= atk_from_larges_reg_weight
turns.append((a_name, b_name, p))
turns = sorted(turns, key=lambda t: t[2], reverse=True)
turns = list(map(lambda t: t[:2], turns))
return turns
def __init__(self, player_name: int, board: Board,
players_order: List[int]) -> None:
"""
Constructs the AI agent for the Dice Wars game.
:param player_name: A name (ID) of the associated player.
:param board: A copy of the game board.
:param players_order: An order in which the players take turn. (A list
of players IDs.)
"""
super().__init__()
self.__player_name = player_name
self.__board = board
self.__players_order = players_order
self.__loger = getLogger(self.__LOGGER_NAME)
self.__model = self.__load_model()
nb_players = board.nb_players_alive()
self.__loger.debug(
f'An AI agent for the {nb_players}-player game is set up.'
f' player_name: {player_name}; players_order: {players_order}')
def __maxn_rec(
self,
board: Board,
players_names: Deque[int],
depth: int,
) -> Tuple[Union[Tuple[int, int], None], Dict[int, float]]:
"""
The Max^n recursive algorithm. It uses the single turn expectiminimax
for the computation of the best moves in the individual turns.
:param board: A copy of the game board.
:param players_names: A queue of players (names of players, i.e., IDs)
to be considered in this algorithm. The player on the top of the
queue is a currently associated player. After a player is
processed, he is removed from the queue.
:param depth: A depth of the algorithm. It declinines with recursive
calls of this method.
:return: A tuple where the first item is a tuple (atacker area,
defender area) with the best turn, or None in case there is
no suitable turn. And the second item of the tuple is
a dictionary where keys are names of players and values
are values of the heuristic function for these players in
a current game board.
"""
# there are no more players
if not players_names:
# just evaluate the heuristic function for the AI's player
return None, self._heuristic([self.__player_name], board)
player_name = players_names[-1]
# current player is not alive => skip him
if not board.get_player_areas(player_name):
players_names.pop()
return self.__maxn_rec(deepcopy(board), players_names, depth)
# depth is not 0 => expand lower nodes
if depth:
# expand some lower suitable nodes, according to the single turn
# expectiminimax, the number of nodes to be expanded is decreasing
# with a depth
turns = self.__possible_turns(player_name, board)[:depth]
if not turns:
# no further moves possible => return just the heuristic
# in a leaf node
_, h = self.__maxn_rec(deepcopy(board), players_names, 0)
return None, h
h = {}
turn = 0, 0
for a, b in turns:
board_copy = deepcopy(board)
self.__perform_atack(board_copy, a, b) # simulate an atack
_, new_h = self.__maxn_rec(board_copy, players_names,
depth - 1)
# maximise the heuristic for a current player
if player_name in new_h:
if player_name not in h \
or new_h[player_name] > h[player_name]:
h = deepcopy(new_h)
turn = a, b
return turn if h else None, h
# depth is 0 => advance to further players or compute the heuristic
players_names.pop()
# there are furthe players
if players_names:
return self.__maxn_rec(deepcopy(board), players_names,
self.__MAXN_DEPTH)
# compute the heuristic function for all living players
living_players = set(a.get_owner_name() for a in board.areas.values())
h = self._heuristic(list(living_players), board)
return None, h
def add_dice_to_player_worst_case(board: Board, player_name: int, is_yourself: bool, logger):
affected_areas: Dict[int, int] = {}
dice = 0
regions = board.get_players_regions(player_name)
for region in regions:
dice = max(dice, len(region))
if is_yourself:
areas = board.get_player_areas(player_name)
b_a = board.get_player_border(player_name)
areas = list(set(areas)-set(b_a))
if areas is None:
areas = b_a
else:
areas = board.get_player_border(player_name)
arr = [a.get_name() for a in areas]
logger.info("PLayer: {} with areas: {}".format(player_name, arr))
if dice > 64:
dice = 64
while dice and areas:
if is_yourself:
area = random.choice(areas)
while area.dice < 8:
area.dice += 1
dice -= 1
else:
area = areas.pop(0)
areas.append(area)
if area.dice >= 8:
areas.remove(area)
elif not is_yourself:
if area.name not in affected_areas:
affected_areas[area.name] = area.dice
area.dice += 1
dice -= 1
if dice > 0:
if is_yourself:
areas = board.get_player_border(player_name)
else:
areas = board.get_player_areas(player_name)
while dice and areas:
area = random.choice(areas)
if area.dice >= 8:
areas.remove(area)
else:
if area.name not in affected_areas:
affected_areas[area.name] = area.dice
area.dice += 1
dice -= 1
try:
yield
finally:
# Vrátit herní pole do původního stavu
for name in affected_areas:
board.get_area(name).dice = affected_areas[name]
