import copy from pysmt.shortcuts import Symbol, Bool, Not, Implies, Iff, And, Or, AtMostOne, ExactlyOne, get_model, get_atoms, get_formula_size, get_unsat_core from pysmt.rewritings import conjunctive_partition import json from typing import List, Optional, Tuple from concurrent.futures import ProcessPoolExecutor from hanabi import DeckCard, Action, ActionType, GameState, HanabiInstance from compress import link, decompress_deck from greedy_solver import GreedyStrategy # literals to model game as sat instance to check for feasibility # variants 'throw it in a hole not handled', 'clue starved' and 'up or down' currently not handled class Literals(): # num_suits is total number of suits, i.e. also counts the dark suits # default distribution among all suits is assumed def __init__(self, instance: HanabiInstance): # clues[m][i] == "after move m we have at least i clues" self.clues = { -1: { i: Bool(i < 9) for i in range(0, 10) } # we have 8 clues after turn -1 , **{ m: { 0: Bool(True), # always at least 0 clues **{ i: Symbol('m{}clues{}'.format(m, i)) for i in range(1, 9) }, 9: Bool(False) # never 9 or more clues. This will implicitly forbid discarding at 8 clues later } for m in range(instance.max_winning_moves) } } # strikes[m][i] == "after move m we have at least i strikes" self.strikes = { -1: {i: Bool(i == 0) for i in range(0, instance.num_strikes + 1)} # no strikes when we start , **{ m: { 0: Bool(True), **{ s: Symbol('m{}strikes{}'.format(m,s)) for s in range(1, instance.num_strikes) }, instance.num_strikes: Bool(False) # never so many clues that we lose. Implicitly forbids striking out } for m in range(instance.max_winning_moves) } } # extraturn[m] = "turn m is a move part of the extra round or a dummy turn" self.extraround = { -1: Bool(False) , **{ m: Bool(False) if m < instance.draw_pile_size else Symbol('m{}extra'.format(m)) # it takes at least as many turns as cards in the draw pile to start the extra round for m in range(0, instance.max_winning_moves) } } # dummyturn[m] = "turn m is a dummy nurn and not actually part of the game" self.dummyturn = { -1: Bool(False) , **{ m: Bool(False) if m < instance.draw_pile_size + instance.num_players else Symbol('m{}dummy'.format(m)) for m in range(0, instance.max_winning_moves) } } # draw[m][i] == "at move m we play/discard deck[i]" self.discard = { m: {i: Symbol('m{}discard{}'.format(m, i)) for i in range(instance.deck_size)} for m in range(instance.max_winning_moves) } # draw[m][i] == "at move m we draw deck card i" self.draw = { -1: { i: Bool(i == instance.num_dealt_cards - 1) for i in range(instance.num_dealt_cards - 1, instance.deck_size) } , **{ m: { instance.num_dealt_cards - 1: Bool(False), **{i: Symbol('m{}draw{}'.format(m, i)) for i in range(instance.num_dealt_cards, instance.deck_size)} } for m in range(instance.max_winning_moves) } } # strike[m] = "at move m we get a strike" self.strike = { -1: Bool(False) , **{ m: Symbol('m{}newstrike'.format(m)) for m in range(instance.max_winning_moves) } } # progress[m][card = (suitIndex, rank)] == "after move m we have played in suitIndex up to rank" self.progress = { -1: {(s, r): Bool(r == 0) for s in range(0, instance.num_suits) for r in range(0, 6)} # at start, have only played rank zero , **{ m: { **{(s, 0): Bool(True) for s in range(0, instance.num_suits)}, **{(s, r): Symbol('m{}progress{}{}'.format(m, s, r)) for s in range(0, instance.num_suits) for r in range(1, 6)} } for m in range(instance.max_winning_moves) } } ## Utility variables # discard_any[m] == "at move m we play/discard a card" self.discard_any = { m: Symbol('m{}discard_any'.format(m)) for m in range(instance.max_winning_moves) } # draw_any[m] == "at move m we draw a card" self.draw_any = {m: Symbol('m{}draw_any'.format(m)) for m in range(instance.max_winning_moves)} # play[m] == "at move m we play a card" self.play = {m: Symbol('m{}play'.format(m)) for m in range(instance.max_winning_moves)} # play5[m] == "at move m we play a 5" self.play5 = {m: Symbol('m{}play5'.format(m)) for m in range(instance.max_winning_moves)} # incr_clues[m] == "at move m we obtain a clue" self.incr_clues = {m: Symbol('m{}c+'.format(m)) for m in range(instance.max_winning_moves)} def solve_sat(starting_state: GameState | HanabiInstance) -> Tuple[bool, Optional[GameState]]: if isinstance(starting_state, HanabiInstance): instance = starting_state game_state = GameState(instance) elif isinstance(starting_state, GameState): instance = starting_state.instance game_state = starting_state else: raise ValueError("Bad argument type") ls = Literals(instance) ##### setup of initial game state # properties used later to model valid moves starting_hands = [[card.deck_index for card in hand] for hand in game_state.hands] first_turn = len(game_state.actions) if isinstance(starting_state, GameState): # have to set additional variables # set initial clues for i in range(0,10): ls.clues[first_turn - 1][i] = Bool(i <= game_state.clues) # set initial strikes for i in range(0, instance.num_strikes + 1): ls.strikes[first_turn - 1][i] = Bool(i <= game_state.strikes) # check if extraround has started (usually not) ls.extraround[first_turn - 1] = Bool(game_state.remaining_extra_turns < game_state.num_players) ls.dummyturn[first_turn -1] = Bool(False) # set recent draws: important to model progress # we just pretend that the last card drawn was in fact drawn last turn, # regardless of when it was actually drawn for neg_turn in range(1, min(9, first_turn + 2)): for i in range(instance.num_players * instance.hand_size, instance.deck_size): ls.draw[first_turn - neg_turn][i] = Bool(neg_turn == 1 and i == game_state.progress - 1) # forbid re-drawing of the last card drawn for m in range(first_turn, instance.max_winning_moves): ls.draw[m][game_state.progress - 1] = Bool(False) # model initial progress for s in range(0, game_state.num_suits): for r in range(0, 6): ls.progress[first_turn - 1][s, r] = Bool(r <= game_state.stacks[s]) ### Now, model all valid moves valid_move = lambda m: And( # in dummy turns, nothing can be discarded Implies(ls.dummyturn[m], Not(ls.discard_any[m])), # definition of discard_any Iff(ls.discard_any[m], Or(ls.discard[m][i] for i in range(instance.deck_size))), # definition of draw_any Iff(ls.draw_any[m], Or(ls.draw[m][i] for i in range(game_state.progress, instance.deck_size))), # ls.draw implies ls.discard (and converse true before the ls.extraround) Implies(ls.draw_any[m], ls.discard_any[m]), Implies(ls.discard_any[m], Or(ls.extraround[m], ls.draw_any[m])), # ls.play requires ls.discard Implies(ls.play[m], ls.discard_any[m]), # definition of ls.play5 Iff(ls.play5[m], And(ls.play[m], Or(ls.discard[m][i] for i in range(instance.deck_size) if instance.deck[i].rank == 5))), # definition of ls.incr_clues Iff(ls.incr_clues[m], And(ls.discard_any[m], Implies(ls.play[m], And(ls.play5[m], Not(ls.clues[m-1][8]))))), # change of ls.clues *[Iff(ls.clues[m][i], Or(ls.clues[m-1][i+1], And(ls.clues[m-1][i], Or(ls.discard_any[m], ls.dummyturn[m])), And(ls.clues[m-1][i-1], ls.incr_clues[m]))) for i in range(1, 9)], ## more than 8 clues not allowed, ls.discarding produces a strike # Note that this means that we will never strike while not at 8 clues. # It's easy to see that if there is any solution to the instance, then there is also one where we only strike at 8 clues # (or not at all) -> Just strike later if neccessary # So, we decrease the solution space with this formulation, but do not change whether it's empty or not Iff(ls.strike[m], And(ls.discard_any[m], Not(ls.play[m]), ls.clues[m-1][8])), # change of strikes *[Iff(ls.strikes[m][i], Or(ls.strikes[m-1][i], And(ls.strikes[m-1][i-1], ls.strike[m]))) for i in range(1, instance.num_strikes + 1)], # less than 0 clues not allowed Implies(Not(ls.discard_any[m]), Or(ls.clues[m-1][1], ls.dummyturn[m])), # we can only draw card i if the last ls.drawn card was i-1 *[Implies(ls.draw[m][i], Or(And(ls.draw[m0][i-1], *[Not(ls.draw_any[m1]) for m1 in range(m0+1, m)]) for m0 in range(max(first_turn - 1, m-9), m))) for i in range(game_state.progress, instance.deck_size)], # we can only draw at most one card (NOTE: redundant, FIXME: avoid quadratic formula) AtMostOne(ls.draw[m][i] for i in range(game_state.progress, instance.deck_size)), # we can only discard a card if we drew it earlier... *[Implies(ls.discard[m][i], Or(ls.draw[m0][i] for m0 in range(m-instance.num_players, first_turn - 1, -instance.num_players))) for i in range(game_state.progress, instance.deck_size)], # ...or if it was part of the initial hand *[Not(ls.discard[m][i]) for i in range(0, game_state.progress) if i not in starting_hands[m % instance.num_players] ], # we can only discard a card if we did not discard it yet *[Implies(ls.discard[m][i], And(Not(ls.discard[m0][i]) for m0 in range(m-instance.num_players, first_turn - 1, -instance.num_players))) for i in range(instance.deck_size)], # we can only discard at most one card (FIXME: avoid quadratic formula) AtMostOne(ls.discard[m][i] for i in range(instance.deck_size)), # we can only play a card if it matches the progress *[Implies( And(ls.discard[m][i], ls.play[m]), And( Not(ls.progress[m-1][instance.deck[i].suitIndex, instance.deck[i].rank]), ls.progress[m-1][instance.deck[i].suitIndex, instance.deck[i].rank-1 ] ) ) for i in range(instance.deck_size) ], # change of progress *[ Iff( ls.progress[m][s, r], Or( ls.progress[m-1][s, r], And(ls.play[m], Or(ls.discard[m][i] for i in range(0, instance.deck_size) if instance.deck[i] == DeckCard(s, r) )) ) ) for s in range(0, instance.num_suits) for r in range(1, 6) ], # extra round bool Iff(ls.extraround[m], Or(ls.extraround[m-1], ls.draw[m-1][instance.deck_size-1])), # dummy turn bool *[Iff(ls.dummyturn[m], Or(ls.dummyturn[m-1], ls.draw[m-1 - instance.num_players][instance.deck_size-1])) for i in range(0,1) if m >= instance.num_players] ) win = And( # maximum progress at each color *[ls.progress[instance.max_winning_moves-1][s, 5] for s in range(0, instance.num_suits)], # played every color/value combination (NOTE: redundant, but makes solving faster) *[ Or( And(ls.discard[m][i], ls.play[m]) for m in range(first_turn, instance.max_winning_moves) for i in range(instance.deck_size) if game_state.deck[i] == DeckCard(s, r) ) for s in range(0, instance.num_suits) for r in range(1, 6) if r > game_state.stacks[s] ] ) constraints = And(*[valid_move(m) for m in range(first_turn, instance.max_winning_moves)], win) # print('Solving instance with {} variables, {} nodes'.format(len(get_atoms(constraints)), get_formula_size(constraints))) model = get_model(constraints) if model: # print_model(model, game_state, ls) solution = evaluate_model(model, copy.deepcopy(game_state), ls) return True, solution else: #conj = list(conjunctive_partition(constraints)) #print('statements: {}'.format(len(conj))) #ucore = get_unsat_core(conj) #print('unsat core size: {}'.format(len(ucore))) #for f in ucore: # print(f.serialize()) return False, None def print_model(model, cur_game_state, ls: Literals): deck = cur_game_state.deck for m in range(ls.max_moves): print('=== move {} ==='.format(m)) print('clues: ' + ''.join(str(i) for i in range(1, 9) if model.get_py_value(ls.clues[m][i]))) print('strikes: ' + ''.join(str(i) for i in range(1, 3) if model.get_py_value(ls.strikes[m][i]))) print('draw: ' + ', '.join('{}: {}'.format(i, deck[i]) for i in range(cur_game_state.progress, 50) if model.get_py_value(ls.draw[m][i]))) print('discard: ' + ', '.join('{}: {}'.format(i, deck[i]) for i in range(50) if model.get_py_value(ls.discard[m][i]))) for s in range(0, ls.num_suits): print('progress {}: '.format(COLORS[s]) + ''.join(str(r) for r in range(1, 6) if model.get_py_value(ls.progress[m][s, r]))) flags = ['discard_any', 'draw_any', 'play', 'play5', 'incr_clues', 'strike', 'extraround', 'dummyturn'] print(', '.join(f for f in flags if model.get_py_value(getattr(ls, f)[m]))) # given the initial game state and the model found by the SAT solver, # evaluates the model to produce a full game history def evaluate_model(model, cur_game_state: GameState, ls: Literals) -> GameState: for m in range(len(cur_game_state.actions), cur_game_state.instance.max_winning_moves): if model.get_py_value(ls.dummyturn[m]) or cur_game_state.is_over(): break if model.get_py_value(ls.discard_any[m]): card_idx = next(i for i in range(0, cur_game_state.instance.deck_size) if model.get_py_value(ls.discard[m][i])) if model.get_py_value(ls.play[m]) or model.get_py_value(ls.strike[m]): cur_game_state.play(card_idx) else: cur_game_state.discard(card_idx) else: cur_game_state.clue() return cur_game_state def run_deck(): puzzle = False if puzzle: deck_str = 'p5 p3 b4 r5 y4 y4 y5 r4 b2 y2 y3 g5 g2 g3 g4 p4 r3 b2 b3 b3 p4 b1 p2 b1 b1 p2 p1 p1 g1 r4 g1 r1 r3 r1 g1 r1 p1 b4 p3 g2 g3 g4 b5 y1 y1 y1 r2 r2 y2 y3' deck = [DeckCard(COLORS.index(c[0]), int(c[1])) for c in deck_str.split(" ")] num_p = 5 else: deck_str = "15gfvqluvuwaqnmrkpkaignlaxpjbmsprksfcddeybfixchuhtwo" deck_str = "15diuknfwhqbplsrlkxjuvfbwyacoaxgtudcerskqfnhpgampmiv" deck_str = "15jdxlpobvikrnhkslcuwggimtphafquqfvcwadampxkeyfrbnsu" deck = decompress_deck(deck_str) num_p = 6 print(deck) gs = GameState(HanabiInstance(deck, num_p)) if puzzle: gs.play(2) else: strat = GreedyStrategy(gs) for _ in range(17): strat.make_move() solvable, sol = solve_sat(gs) if solvable: print(sol) print(link(sol)) else: print('unsolvable') if __name__ == "__main__": run_deck()