def make_pr_domain_file(self):
hyp_problem = 'hyp_%d_problem.pddl' % 9999
self.generate_pddl_for_hyp_plan(hyp_problem)
# EXPLAIN: definition 2, (Ramirez & Geffner 2010)
# derive problem with G_Obs
trans_cmd = translation.Probabilistic_PR('domain.pddl', hyp_problem,
'obs.dat')
trans_cmd.execute()
path = os.getcwd() + "/prob-PR/O/pr-domain.pddl " + os.getcwd()
os.system("mv " + path)
path = os.getcwd() + "/prob-PR/O/pr-problem.pddl " + os.getcwd()
os.system("mv " + path)
# erase folder after done unpacking
os.system("rm " + "prob-PR")
def run_planner_on_obs(self, obs_index):
# os.chdir(ROOT_DIR)
obs_plans = []
obs_actions = []
for i in range(obs_index):
hyp_problem = 'obs_%d_problem.pddl' % i
self.modify_obs_file(i)
# creating the derived problem with G_Obs
trans_cmd = translation.Probabilistic_PR('domain.pddl',
hyp_problem, 'obs.dat')
trans_cmd.execute()
os.system('mv prob-PR obs_prob-%s-PR' % i)
for id, domain, instance in self.walk('obs_prob-%s-PR' % i):
if id == "O":
plan_for_G_Obs_cmd = planners.HSP(domain, instance, i)
plan_for_G_Obs_cmd.execute()
obs_actions = plan_for_G_Obs_cmd.get_plan()
obs_plans.append(obs_actions)
return obs_plans
def __init__(self, domain_name, hyps=[]):
self.domain_name = domain_name
self.has_duplicates = False
self.actions = []
self.predicates = []
self.actions_dict = {}
self.predicates_dict = {}
# actions done in hyp path
self.fulfilled_actions = []
self.fulfilled_predicates = []
self.seen_in_path_actions = []
self.actions_in_cost = []
self.cost = 0
# actions done in obs path
self.obs_fulfilled_actions = []
self.obs_fulfilled_predicates = []
self.obs_seen_actions = []
self.obs_in_cost = []
self.obs_in_process_actions = []
self.obs_in_process_preconditions = []
self.obs_cost = 0
self.data_saved = {}
self.temp_fullfilled = []
self.in_process_actions = []
self.in_process_preconditions = []
# creating full pr-domain file
trans_cmd = translation.Probabilistic_PR('domain.pddl',
'pr-domain.pddl', 'obs.dat')
trans_cmd.execute()
# parse the domain to our format
if "intrusion" in domain_name or "block" in domain_name:
self.intrusion()
elif "easy" in domain_name or "bui" in domain_name or "logistics" in domain_name:
self.easy()
def test_for_sim(self, index, options):
import math, csv
#EXPLAIN: definition 1, (Ramirez & Geffner 2010)
# generate the problem with G=H
hyp_problem = 'hyp_%d_problem.pddl' % index
self.generate_pddl_for_hyp_plan(hyp_problem)
# EXPLAIN: definition 2, (Ramirez & Geffner 2010)
# derive problem with G_Obs
trans_cmd = translation.Probabilistic_PR('domain.pddl', hyp_problem,
'obs.dat')
#trans_cmd.convert_to_integers = True
trans_cmd.factor = 1000.0
trans_cmd.execute()
self.trans_time = trans_cmd.time
os.system('mv prob-PR prob-%s-PR' % index)
self.costs = dict()
G_Obs_time = 0.0
min_cost = 1e7
# EXPLAIN: creates appropriate planner according to the flags in option.py file
for id, domain, instance in self.walk('prob-%s-PR' % index):
if options.optimal:
plan_for_G_Obs_cmd = planners.H2(domain, instance, index,
options.max_time,
options.max_memory)
else:
if options.use_hspr:
plan_for_G_Obs_cmd = planners.HSPr(domain, instance, index,
options.max_time,
options.max_memory)
elif options.use_FF:
plan_for_G_Obs_cmd = planners.Metric_FF(
domain, instance, index, options.max_time,
options.max_memory)
else:
plan_for_G_Obs_cmd = planners.LAMA(domain, instance, index,
options.max_time,
options.max_memory)
self.greedy = True
# EXPLAIN: run the planner
plan_for_G_Obs_cmd.execute()
# EXPLAIN: return if fails
if plan_for_G_Obs_cmd.signal != 0 and plan_for_G_Obs_cmd.signal != 256:
self.test_failed = True
return
# EXPLAIN: not failed, update time
G_Obs_time += plan_for_G_Obs_cmd.time
# EXPLAIN: calculate plans with obs
if id == 'O':
self.Plan_Time_O = plan_for_G_Obs_cmd.time
if self.greedy:
if not os.path.exists(
"temp_results/ideal_temp_files/soln_files"):
os.makedirs("temp_results/ideal_temp_files/soln_files",
mode=0777)
command = "cp pr-problem.soln temp_results/ideal_temp_files/soln_files/pr-problem_" + str(
index) + ".soln"
os.system(command)
# EXPLAIN: calculate complimentory plans (without obs)
if id == 'neg-O': self.Plan_Time_Not_O = plan_for_G_Obs_cmd.time
# EXPLAIN: update minimal cost
self.costs[id] = plan_for_G_Obs_cmd.cost / trans_cmd.factor
if self.costs[id] < min_cost:
min_cost = self.costs[id]
print >> sys.stdout, "Min Cost:", min_cost
print >> sys.stdout, "Costs:", self.costs
self.plan_time = G_Obs_time
self.total_time = trans_cmd.time + self.plan_time
# EXPLAIN: definition 4 and 5, (Ramirez & Geffner 2010)
# P(O|G) / P( \neg O | G) = exp { -beta Delta(G,O) }
# Delta(G,O) = cost(G,O) - cost(G,\neg O)
# EXPLAIN: delta(G|O)
try:
likelihood_ratio = math.exp(
-options.beta * (self.costs['O'] - self.costs['neg-O']))
except Exception:
likelihood_ratio = 0
# likelihood_ratio = math.exp( -options.beta*(self.costs['O']-self.costs['neg-O']) )
# P(O|G) = exp { -beta Delta(G,O) } / 1 + exp { -beta Delta(G,O) }
# EXPLAIN: (O|G)
self.Probability_O = likelihood_ratio / (1.0 + likelihood_ratio)
# EXPLAIN: (NEG-O|G)
self.Probability_Not_O = 1.0 - self.Probability_O
self.cost_O = self.costs['O']
self.cost_Not_O = self.costs['neg-O']
def test_offline(self, index, max_time, max_mem, optimal=False, beta=1.0):
# EXPLAIN: definition 1, (Ramirez & Geffner 2010)
# generate the problem with G=H
hyp_problem = 'hyp_%d_problem.pddl' % index
self.generate_pddl_for_hyp_plan(hyp_problem)
# EXPLAIN: definition 2, (Ramirez & Geffner 2010)
# derive problem with G_Obs
trans_cmd = translation.Probabilistic_PR('domain.pddl', hyp_problem,
'obs.dat')
trans_cmd.execute()
self.trans_time = trans_cmd.time
os.system('mv prob-PR prob-%s-PR' % index)
self.costs = dict()
G_Obs_time = 0.0
min_cost = 1e7
time_bound = max_time
# EXPLAIN: if optimal flag in options.py was risen
if optimal:
time_bound = max_time / 2
for id, domain, instance in self.walk('prob-%s-PR' % index):
# EXPLAIN: creates an HSP planner
plan_for_G_Obs_cmd = planners.HSP(domain, instance, index,
time_bound, max_mem)
# EXPLAIN: run the planner
plan_for_G_Obs_cmd.execute()
# EXPLAIN: calculate plans with obs
if id == 'O':
self.Plan_Time_O = plan_for_G_Obs_cmd.time
# EXPLAIN: calculate complimentory plans (without obs)
if id == 'neg-O':
self.Plan_Time_Not_O = plan_for_G_Obs_cmd.time
# EXPLAIN: update time
G_Obs_time += plan_for_G_Obs_cmd.time
self.costs[id] = plan_for_G_Obs_cmd.cost
# EXPLAIN: update minimal cost
if plan_for_G_Obs_cmd.cost < min_cost:
min_cost = plan_for_G_Obs_cmd.cost
# Save solution files
if id == 'O' or id == 'neg-O':
save_sol_file(id, index, "offline")
# EXPLAIN: if optimal flag in options.py was not risen
if not optimal:
# time_bound = max_time / 3
# plan_for_G_cmd = planners.LAMA( 'domain.pddl', hyp_problem, index, time_bound, max_mem )
# plan_for_G_cmd.execute()
# if plan_for_G_cmd.cost < min_cost :
# min_cost = plan_for_G_cmd.cost
# remainder = time_bound - plan_for_G_cmd.time
# print >> sys.stdout, "Time remaining:", time_bound
# if remainder > 0 :
# time_bound = (max_time / 3 ) + (remainder / 2 )
time_bound = max_time / 2
# EXPLAIN: creates an LAMA planner
for id, domain, instance in self.walk('prob-%s-PR' % index):
plan_for_G_Obs_cmd = planners.LAMA(domain, instance, index,
time_bound, max_mem)
# EXPLAIN: run the planner
plan_for_G_Obs_cmd.execute()
# EXPLAIN: not failed, update time
G_Obs_time += plan_for_G_Obs_cmd.time
# EXPLAIN: calculate plans with obs
if id == 'O':
self.Plan_Time_O = plan_for_G_Obs_cmd.time
# EXPLAIN: calculate complimentory plans (without obs)
if id == 'neg-O':
self.Plan_Time_Not_O = plan_for_G_Obs_cmd.time
remainder = time_bound - plan_for_G_Obs_cmd.time
if remainder > 0:
time_bound = time_bound + remainder
# EXPLAIN: update minimal cost
self.costs[id] = plan_for_G_Obs_cmd.cost
if plan_for_G_Obs_cmd.cost < min_cost:
min_cost = plan_for_G_Obs_cmd.cost
# Save solution files
if id == 'O' or id == 'neg-O':
save_sol_file(id, index, "offline")
self.plan_time = G_Obs_time
self.total_time = trans_cmd.time + self.plan_time
# EXPLAIN: definition 4 and 5, (Ramirez & Geffner 2010)
# P(O|G) / P( \neg O | G) = exp { -beta Delta(G,O) }
# Delta(G,O) = cost(G,O) - cost(G,\neg O)
# EXPLAIN: delta(G|O)
try:
likelihood_ratio = math.exp(
-beta * (self.costs['O'] - self.costs['neg-O']))
except Exception:
likelihood_ratio = 0
# likelihood_ratio = math.exp( -beta*(self.costs['O']-self.costs['neg-O']) )
# P(O|G) = exp { -beta Delta(G,O) } / 1 + exp { -beta Delta(G,O) }
# EXPLAIN: (O|G)
self.Probability_O = likelihood_ratio / (1.0 + likelihood_ratio)
# EXPLAIN: (NEG-O|G)
self.Probability_Not_O = 1.0 - self.Probability_O
self.cost_O = self.costs['O']
self.cost_Not_O = self.costs['neg-O']
if "H" in self.name:
path = os.getcwd() + "/" + 'pr-problem.soln'
self.offline_ideal_cost = self.get_greedy_cost(path, self.name)
def test_online(self,
index,
obs_index,
max_time,
max_mem,
optimal=False,
beta=1.0):
# generate the problem with G=H
hyp_problem = 'hyp_%d_problem.pddl' % index
self.generate_pddl_for_hyp_plan(hyp_problem)
global count
if obs_index > count:
count += 1
self.curr_obs_num = count + 1
modify_obs_file(count)
# creating the derived problem with G_Obs
trans_cmd = translation.Probabilistic_PR('domain.pddl', hyp_problem,
'obs.dat')
trans_cmd.execute()
self.trans_time = trans_cmd.time
os.system('mv prob-PR prob-%s-PR' % index)
self.costs = dict()
G_Obs_time = 0.0
min_cost = 1e7
time_bound = max_time
# modifying init in pr_domain
for id, domain, instance in self.walk('prob-%s-PR' % index):
if id == 'O':
self.modify_init_pr_problem_file(self.domain, instance)
if optimal:
time_bound = max_time / 2
for id, domain, instance in self.walk('prob-%s-PR' % index):
plan_for_G_Obs_cmd = planners.HSP(domain, instance, index,
time_bound, max_mem)
plan_for_G_Obs_cmd.execute()
if id == 'O':
self.Plan_Time_O = plan_for_G_Obs_cmd.time
if self.greedy:
if not os.path.exists(
"temp_results/ideal_temp_files/soln_files"):
os.makedirs(
"temp_results/ideal_temp_files/soln_files",
mode=0777)
command = "cp pr-problem.soln temp_results/ideal_temp_files/soln_files/pr-problem_" + str(
index) + ".soln"
os.system(command)
if id == 'neg-O':
self.Plan_Time_Not_O = plan_for_G_Obs_cmd.time
G_Obs_time += plan_for_G_Obs_cmd.time
self.costs[id] = plan_for_G_Obs_cmd.cost
if plan_for_G_Obs_cmd.cost < min_cost:
min_cost = plan_for_G_Obs_cmd.cost
# Save solution files
if id == 'O' or id == 'neg-O':
save_sol_file(id, index, "online", obs_index)
self.path = plan_for_G_Obs_cmd.get_plan()
if not optimal:
#time_bound = max_time / 3
#plan_for_G_cmd = planners.LAMA( 'domain.pddl', hyp_problem, index, time_bound, max_mem )
#plan_for_G_cmd.execute()
#if plan_for_G_cmd.cost < min_cost :
# min_cost = plan_for_G_cmd.cost
#remainder = time_bound - plan_for_G_cmd.time
#print >> sys.stdout, "Time remaining:", time_bound
#if remainder > 0 :
# time_bound = (max_time / 3 ) + (remainder / 2 )
time_bound = max_time / 2
for id, domain, instance in self.walk('prob-%s-PR' % index):
plan_for_G_Obs_cmd = planners.LAMA(domain, instance, index,
time_bound, max_mem)
plan_for_G_Obs_cmd.execute()
G_Obs_time += plan_for_G_Obs_cmd.time
if id == 'O':
self.Plan_Time_O = plan_for_G_Obs_cmd.time
if self.greedy:
if not os.path.exists(
"temp_results/ideal_temp_files/soln_files"):
os.makedirs(
"temp_results/ideal_temp_files/soln_files",
mode=0777)
command = "cp pr-problem.soln temp_results/ideal_temp_files/soln_files/pr-problem_" + str(
index) + ".soln"
os.system(command)
if id == 'neg-O':
self.Plan_Time_Not_O = plan_for_G_Obs_cmd.time
remainder = time_bound - plan_for_G_Obs_cmd.time
if remainder > 0:
time_bound = time_bound + remainder
self.costs[id] = plan_for_G_Obs_cmd.cost
if plan_for_G_Obs_cmd.cost < min_cost:
min_cost = plan_for_G_Obs_cmd.cost
# Save solution files
if id == 'O' or id == 'neg-O':
save_sol_file(id, index, "online", obs_index)
self.plan_time = G_Obs_time
self.total_time = trans_cmd.time + self.plan_time
# P(O|G) / P( \neg O | G) = exp { -beta Delta(G,O) }
# Delta(G,O) = cost(G,O) - cost(G,\neg O)
try:
likelihood_ratio = math.exp(
-beta * (self.costs['O'] - self.costs['neg-O']))
except Exception:
likelihood_ratio = 0
# likelihood_ratio = math.exp( -beta*(self.costs['O']-self.costs['neg-O']) )
# P(O|G) = exp { -beta Delta(G,O) } / 1 + exp { -beta Delta(G,O) }
self.Probability_O = likelihood_ratio / (1.0 + likelihood_ratio)
self.Probability_Not_O = 1.0 - self.Probability_O
self.cost_O = self.costs['O']
self.cost_Not_O = self.costs['neg-O']
if "H" in self.name:
path = os.getcwd() + "/" + 'pr-problem.soln'
self.offline_ideal_cost = self.get_greedy_cost(path, self.name)
def test(self, index, max_time, max_mem, optimal=False, beta=1.0):
import math, csv
# generate the problem with G=H
hyp_problem = 'hyp_%d_problem.pddl' % index
self.generate_pddl_for_hyp_plan(hyp_problem)
# derive problem with G_Obs
trans_cmd = translation.Probabilistic_PR('domain.pddl', hyp_problem,
'obs.dat')
trans_cmd.execute()
self.trans_time = trans_cmd.time
os.system('mv prob-PR prob-%s-PR' % index)
self.costs = dict()
G_Obs_time = 0.0
min_cost = 1e7
time_bound = max_time
if optimal:
time_bound = max_time / 2
for id, domain, instance in self.walk('prob-%s-PR' % index):
plan_for_G_Obs_cmd = planners.HSP(domain, instance, index,
time_bound, max_mem)
plan_for_G_Obs_cmd.execute()
if id == 'O': self.Plan_Time_O = plan_for_G_Obs_cmd.time
if id == 'neg-O':
self.Plan_Time_Not_O = plan_for_G_Obs_cmd.time
G_Obs_time += plan_for_G_Obs_cmd.time
self.costs[id] = plan_for_G_Obs_cmd.cost
if plan_for_G_Obs_cmd.cost < min_cost:
min_cost = plan_for_G_Obs_cmd.cost
if not optimal:
#time_bound = max_time / 3
#plan_for_G_cmd = planners.LAMA( 'domain.pddl', hyp_problem, index, time_bound, max_mem )
#plan_for_G_cmd.execute()
#if plan_for_G_cmd.cost < min_cost :
# min_cost = plan_for_G_cmd.cost
#remainder = time_bound - plan_for_G_cmd.time
#print >> sys.stdout, "Time remaining:", time_bound
#if remainder > 0 :
# time_bound = (max_time / 3 ) + (remainder / 2 )
time_bound = max_time / 2
for id, domain, instance in self.walk('prob-%s-PR' % index):
plan_for_G_Obs_cmd = planners.FastDownward_LAMA(
domain, instance, index, time_bound, max_mem
) #Michael changed standard Lama to Fastdownward Lama
plan_for_G_Obs_cmd.execute()
G_Obs_time += plan_for_G_Obs_cmd.time
if id == 'O': self.Plan_Time_O = plan_for_G_Obs_cmd.time
if id == 'neg-O':
self.Plan_Time_Not_O = plan_for_G_Obs_cmd.time
remainder = time_bound - plan_for_G_Obs_cmd.time
if remainder > 0:
time_bound = time_bound + remainder
self.costs[id] = plan_for_G_Obs_cmd.cost
if plan_for_G_Obs_cmd.cost < min_cost:
min_cost = plan_for_G_Obs_cmd.cost
self.plan_time = G_Obs_time
self.total_time = trans_cmd.time + self.plan_time
# P(O|G) / P( \neg O | G) = exp { -beta Delta(G,O) }
# Delta(G,O) = cost(G,O) - cost(G,\neg O)
likelihood_ratio = math.exp(-beta *
(self.costs['O'] - self.costs['neg-O']))
# P(O|G) = exp { -beta Delta(G,O) } / 1 + exp { -beta Delta(G,O) }
self.Probability_O = likelihood_ratio / (1.0 + likelihood_ratio)
self.Probability_Not_O = 1.0 - self.Probability_O
self.cost_O = self.costs['O']
self.cost_Not_O = self.costs['neg-O']
def test_for_sim(self, index, options):
import math, csv
# generate the problem with G=H
hyp_problem = 'hyp_%d_problem.pddl' % index
self.generate_pddl_for_hyp_plan(hyp_problem)
# derive problem with G_Obs
trans_cmd = translation.Probabilistic_PR('domain.pddl', hyp_problem,
'obs.dat')
#trans_cmd.convert_to_integers = True
trans_cmd.factor = 1000.0
trans_cmd.execute()
self.trans_time = trans_cmd.time
os.system('mv prob-PR prob-%s-PR' % index)
self.costs = dict()
G_Obs_time = 0.0
min_cost = 1e7
for id, domain, instance in self.walk('prob-%s-PR' % index):
if options.optimal:
plan_for_G_Obs_cmd = planners.H2(domain, instance, index,
options.max_time,
options.max_memory)
else:
if options.use_hspr:
plan_for_G_Obs_cmd = planners.HSPr(domain, instance, index,
options.max_time,
options.max_memory)
elif options.use_FF:
plan_for_G_Obs_cmd = planners.Metric_FF(
domain, instance, index, options.max_time,
options.max_memory)
else:
plan_for_G_Obs_cmd = planners.LAMA(
domain, instance, index, options.max_time,
options.max_memory
) #Michael changed standard Lama to Fastdownward Lama
plan_for_G_Obs_cmd.execute()
if plan_for_G_Obs_cmd.signal != 0 and plan_for_G_Obs_cmd.signal != 256:
self.test_failed = True
return
G_Obs_time += plan_for_G_Obs_cmd.time
if id == 'O': self.Plan_Time_O = plan_for_G_Obs_cmd.time
if id == 'neg-O': self.Plan_Time_Not_O = plan_for_G_Obs_cmd.time
self.costs[id] = plan_for_G_Obs_cmd.cost / trans_cmd.factor
if self.costs[id] < min_cost:
min_cost = self.costs[id]
print >> sys.stdout, "Min Cost:", min_cost
print >> sys.stdout, "Costs:", self.costs
self.plan_time = G_Obs_time
self.total_time = trans_cmd.time + self.plan_time
# P(O|G) / P( \neg O | G) = exp { -beta Delta(G,O) }
# Delta(G,O) = cost(G,O) - cost(G,\neg O)
likelihood_ratio = math.exp(-options.beta *
(self.costs['O'] - self.costs['neg-O']))
# P(O|G) = exp { -beta Delta(G,O) } / 1 + exp { -beta Delta(G,O) }
self.Probability_O = likelihood_ratio / (1.0 + likelihood_ratio)
self.Probability_Not_O = 1.0 - self.Probability_O
self.cost_O = self.costs['O']
self.cost_Not_O = self.costs['neg-O']