def proccess_sat_file(filename, sat, solver):
start_time = time.time()
result = "{} || solver {} ".format(filename, solver)
formula = CNF(from_file="./InstanciasSAT/" + filename)
clauses = formula.clauses[:]
nv = formula.nv
original_time = time.time()
original_solution = solve_clauses(clauses, solver)
result += "|| original: {} || Tiempo: {:.10f} segundos ".format(original_solution, time.time() - original_time)
clauses, nv = sat_to_3_sat(clauses, nv)
if sat > 3:
x_sat = 3
while x_sat < sat:
clauses, nv = reduce_to_x_sat(clauses, nv)
x_sat += 1
x_sat_time = time.time()
x_sat_solution = solve_clauses(clauses, solver)
result += "|| {}-SAT: {} || Tiempo: {:.10f} segundos ".format(sat, x_sat_solution, time.time() - x_sat_time)
formula.clauses = clauses
formula.nv = nv
formula.to_file("./X-SAT/" + filename)
result += "|| Tiempo total: {:.10f} segundos".format(time.time() - start_time)
print(result)
def get_sat_problem(name):
path = PATH+'sat_problems/'+name+".tar.gz"
tar = tarfile.open(path)
f = tar.extractfile(random.choice(tar.getmembers()))
content = f.read()
formula = CNF(from_string=content.decode("utf-8"))
formula.clauses = formula.clauses[:-2]
return formula
def solve_problem(problem):
p_atoms, last = enumerate_states(problem, 1)
a_atoms, last = enumerate_agent_actions(last, problem)
spa_atoms, last = enumerate_spread_actions(problem, last)
agea_atoms, last = enumerate_states(problem, last)
noopa_atoms, last = enumerate_states(problem, last)
observation_clauses = gen_observation_clauses(p_atoms,
problem['observations'])
action_preconditions_clauses = gen_precondition_clauses(
p_atoms, a_atoms, spa_atoms, noopa_atoms, agea_atoms)
fact_acheivement_clauses = gen_fact_acheivement_clauses(
p_atoms, a_atoms, spa_atoms, noopa_atoms, agea_atoms)
action_interefernce_clauses = gen_action_interefernce_clauses(
a_atoms, spa_atoms, noopa_atoms, agea_atoms)
must_spread_clauses = gen_must_spread_clauses(p_atoms, a_atoms, spa_atoms)
must_age_clauses = gen_must_age_clauses(p_atoms, a_atoms, agea_atoms)
S_representation_clauses = gen_S_representation_clauses(p_atoms)
must_use_teams_clauses = gen_must_use_teams_clauses(
p_atoms, a_atoms, problem['medics'], problem['police'])
limited_teams_clauses = gen_limited_teams_clauses(p_atoms, a_atoms,
problem['medics'],
problem['police'])
all_clauses = observation_clauses + action_preconditions_clauses + fact_acheivement_clauses + action_interefernce_clauses + must_spread_clauses + must_age_clauses + S_representation_clauses + must_use_teams_clauses + limited_teams_clauses
answer = {}
non_starter_list = ['I', 'Q']
for query in problem['queries']:
row = query[0][0]
col = query[0][1]
step = query[1]
status = query[2]
if step == 0 and status in non_starter_list:
answer[
query] = 'F' # a query about a state that can't be in step 0 returns 'F' instantly
else:
if status == 'S':
query_clause = [
p_atoms['S3'][step][row][col],
p_atoms['S2'][step][row][col],
p_atoms['S1'][step][row][col]
]
elif status == 'Q':
if step == 0: # can't have Q in initial conditions
query_clause = [False]
else:
query_clause = [
p_atoms['Q2'][step][row][col],
p_atoms['Q1'][step][row][col]
]
elif status == 'I':
if step == 0: # can't have I in initial conditions
query_clause = [False]
else:
query_clause = [p_atoms['I'][step][row][col]]
else:
query_clause = [p_atoms[status][step][row][col]]
cnf_formula1 = CNF()
sat_solver = Solver()
cnf_formula1.clauses = all_clauses + [query_clause]
#cnf_formula.clauses = all_clauses + [[21, 29, 37]]
sat_solver.append_formula(cnf_formula1)
res1 = sat_solver.solve()
#res2 = sat_solver.get_model()
sat_solver.delete()
res1_other = False
if res1 == True:
# try to solve again- with any otehr possible status instead of status
# If there is a solution- then it means there could be another status
# It this case the result can;t be conclusive
sat_solver = Solver()
negative_query_clauses = []
for q in query_clause:
negative_query_clauses.append([-q])
cnf_formula2 = CNF()
cnf_formula2.clauses = all_clauses + negative_query_clauses
sat_solver.append_formula(cnf_formula2)
res1_other = sat_solver.solve()
model = sat_solver.get_model()
sat_solver.delete()
if res1 and not res1_other: # The query status is possible and no other status is possible
answer[query] = 'T'
elif res1 and res1_other: # The query status is possible but also at least one more status can be
answer[query] = '?'
else:
answer[query] = 'F' # The query status is not possible
sat_solver.delete()
return answer
