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# coding: utf-8 import os, sys, subprocess import time from z3 import *
CHOICES = ["Rock", "Paper", "Scissors", "Spock", "Lizard"]
def next_random_number(s_i): return ((11 * s_i) + 12345) & 0x7fff
class RPSSLComputer: def __init__(self, s0): self.previous_random_number = s0
def compute_choice(self): random_number = next_random_number(self.previous_random_number) self.previous_random_number = random_number return random_number % 5, CHOICES[random_number % 5]
# cf. https://bigbangtheory.fandom.com/wiki/Rock,_Paper,_Scissors,_Lizard,_Spock def winning_mapping(i): if i == 0: return 1#"Paper" if i == 1: return 2#"Scissors" if i == 2: return 0#"Rock" if i == 3: return 4#"Lizard" if i == 4: return 0#"Rock" return "Did you forget to compute the remainder modulo 5?"
def compute_winner(computer, player): if computer == player: return "\tTie.", False is_player_bigger = True if player > computer else False absolute_difference = abs(computer - player) if absolute_difference % 2 == 1: if is_player_bigger: return "\tPlayer wins.", True else: return "\tComputer wins.", False else: if is_player_bigger: return "\tComputer wins.", False else: return "\tPlayer wins.", True
solver = Solver() states = list()
# We are adding the first state s_0 to the list of states states.append(BitVec("state0", 16))
s0 = int(sys.argv[1]) computer = RPSSLComputer(s0)
preprocess_count = 5
def add_constraint(solver, index, computers_choice): # TODO create a BitVec and append it to the list of states # You might want to call it state{index}
# TODO Enforce that the newly added BitVec-variable must evaluate to the result of the LCG computation using the previous result
# TODO Enforce that the unsigned remainder of the newly added BitVec-varialbe and 5 evaluates to the choice of the computer
pass
def store_backtracking_point(solver): solver.push()
def restore_backtracking_point(solver): solver.pop()
def add_next_state_constraint(solver): s_i_plus_1 = BitVec("s_i_plus_1", 16) # TODO Enforce that the next state value is computed via the same computation as above
return s_i_plus_1
def get_players_choice(solver, s_i_plus_1): # TODO Get the value of next_state from the model and return it modulo 5 # Hint: winning_mapping(...) returns a good answer for the computer's choice # Hint: use solver.model() like a python dict. # Hint: use `.as_long()` to convert a z3 variable to a python integer value return 0
# Main loop:
# We read preprocess_count many choices from the computer before we start to ask z3 for a solution # Note that for these preprocessing rounds we do not need to make a good guess and compute the winner # We are only interested in what the computer picks for the first few rounds for index in range(1,preprocess_count): computer_choice, _ = computer.compute_choice() player_choice = 0 # We always choose Rock since we cannot make good guesses in the beginning
add_constraint(solver,index,computer_choice) output, won = compute_winner(computer_choice, player_choice) print(output) if won: print("Congratulations!") else: print("Try again.")
# Now we start by adding a 'special' variable s_i_plus_1 and try to win index = preprocess_count while True: store_backtracking_point(solver) s_i_plus_1 = add_next_state_constraint(solver) solver.check()
player_choice = get_players_choice(solver, s_i_plus_1) computer_choice, _ = computer.compute_choice() output, won = compute_winner(computer_choice, player_choice)
print(output) if won: print("Congratulations!") else: print("Try again.")
restore_backtracking_point(solver) add_constraint(solver, index, computer_choice) # input("") index += 1 if index >= 100: break
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