def main():
options, args = parse_options()
check_python_version(options.force_old_python)
timer = timers.Timer()
with timers.timing("Parsing", True):
task = pddl.open()
with timers.timing("Normalizing task"):
normalize.normalize(task)
if options.generate_relaxed_task:
# Remove delete effects.
for action in task.actions:
for index, effect in reversed(list(enumerate(action.effects))):
if effect.literal.negated:
del action.effects[index]
sas_task = pddl_to_sas(task)
dump_statistics(sas_task)
with timers.timing("Writing output"):
with open("output.sas", "w") as output_file:
sas_task.output(output_file)
print("Done! %s" % timer)
def main() :
task = pddl.open()
relaxed_solvable, atoms, actions, axioms = instantiate.explore(task)
#for action in actions :
# action.dump()
not_trivial_axioms = []
trivial_axioms = {}
for axiom in axioms :
if len(axiom.condition) > 1 :
not_trivial_axioms.append( axiom )
continue
if not isinstance( axiom.effect, pddl.conditions.Literal ) :
not_trivial_axioms.append( axiom )
continue
if isinstance( axiom.effect, pddl.conditions.NegatedAtom ) :
not_trivial_axioms.append( axiom )
continue
try :
trivial_axioms[ axiom.effect ].append( axiom.condition[0] )
except KeyError :
trivial_axioms[ axiom.effect ] = [ axiom.condition[0] ]
print >> sys.stdout, "Non-trivial axioms:", len(not_trivial_axioms)
print >> sys.stdout, "Trivial axioms:", len(trivial_axioms.keys())
if not relaxed_solvable :
print >> sys.stdout, "Problem is unsolvable!"
sys.exit(1)
task_to_prop_strips_domain( task, atoms, actions, not_trivial_axioms )
task_to_prop_strips_problem( task, atoms, trivial_axioms )
def main():
print('HOLAAAAAAAAAAAAAAAAAAAAAAA')
args = parse_args()
timer = timers.Timer()
with timers.timing("Parsing", True):
task = pddl.open(task_filename=args.task, domain_filename=args.domain)
with timers.timing("Normalizing task"):
normalize.normalize(task)
task.INVARIANT_TIME_LIMIT = int(args.inv_limit)
if args.generate_relaxed_task:
# Remove delete effects.
for action in task.actions:
for index, effect in reversed(list(enumerate(action.effects))):
if effect.literal.negated:
del action.effects[index]
sas_task = pddl_to_sas(task)
assert len(sas_task.operators) == len(set([o.name for o in sas_task.operators])), \
"Error: Operator names (with parameters) must be unique"
dump_statistics(sas_task)
with timers.timing("Writing output"):
with open(args.sas_name, "w") as output_file:
sas_task.output(output_file)
print("Done! %s" % timer)
def main():
args = parse_args()
timer = timers.Timer()
with timers.timing("Parsing", True):
task = pddl.open(task_filename=args.task,
domain_filename=args.domain,
addl_filename=args.addl)
with timers.timing("Normalizing task"):
normalize.normalize(task)
if args.generate_relaxed_task:
# Remove delete effects.
for action in task.actions:
for index, effect in reversed(list(enumerate(action.effects))):
if effect.literal.negated:
del action.effects[index]
output_file = args.output_file
use_proto = args.use_proto
print('Use Proto:', use_proto)
sas_task = pddl_to_sas(task, args.agent_id, args.agent_url)
dump_statistics(sas_task)
with timers.timing("Writing output"):
with open(output_file, "w") as output_file:
if use_proto:
sas_task.output_proto(output_file)
else:
sas_task.output(output_file)
print("Done! %s" % timer)
def main():
args = parse_args()
timer = timers.Timer()
with timers.timing("Parsing", True):
task = pddl.open(task_filename=args.task,
domain_filename=args.domain,
addl_filename=args.addl)
with timers.timing("Normalizing task"):
normalize.normalize(task)
if args.generate_relaxed_task:
# Remove delete effects.
for action in task.actions:
for index, effect in reversed(list(enumerate(action.effects))):
if effect.literal.negated:
del action.effects[index]
output_file = args.output_file
use_proto = args.use_proto
print('Use Proto:', use_proto)
sas_task = pddl_to_sas(task, args.agent_id, args.agent_url)
dump_statistics(sas_task)
with timers.timing("Writing output"):
with open(output_file, "w") as output_file:
if use_proto:
sas_task.output_proto(output_file)
else:
sas_task.output(output_file)
print("Done! %s" % timer)
def main():
args = parse_args()
timer = timers.Timer()
with timers.timing("Parsing", True):
task = pddl.open(task_filename=args.task, domain_filename=args.domain)
with timers.timing("Normalizing task"):
normalize.normalize(task)
if args.generate_relaxed_task:
# Remove delete effects.
for action in task.actions:
for index, effect in reversed(list(enumerate(action.effects))):
if effect.literal.negated:
del action.effects[index]
sas_task = pddl_to_sas(task)
dump_statistics(sas_task)
if not sas_task is None:
with timers.timing("Writing output"):
with open("output.sas", "w") as output_file:
sas_task.output(output_file)
print("Done! %s" % timer)
def main():
print("--------------FOND Translator------------")
args = parse_args()
timer = timers.Timer()
with timers.timing("Parsing", True):
task = pddl.open(task_filename=args.task, domain_filename=args.domain)
# for test:
print();
print("Problem Filename = " + args.task);
print("Domain Filename = " + args.domain);
print();
with timers.timing("Normalizing task"):
normalize.normalize(task)
if args.generate_relaxed_task:
# Remove delete effects.
for action in task.actions:
for index, effect in reversed(list(enumerate(action.effects))):
if effect.literal.negated:
del action.effects[index]
sas_task = pddl_to_sas(task)
dump_statistics(sas_task)
with timers.timing("Writing output"):
with open("..\\webapps\\LunaPlanner\\translator_output\\output.sas", "w") as output_file:
sas_task.output(output_file)
print()
print("SAS file saved at: " + output_file.name)
print("Done! %s" % timer)
def main():
options, args = parse_options()
check_python_version(options.force_old_python)
timer = timers.Timer()
with timers.timing("Parsing", True):
task = pddl.open()
with timers.timing("Normalizing task"):
normalize.normalize(task)
if options.generate_relaxed_task:
# Remove delete effects.
for action in task.actions:
for index, effect in reversed(list(enumerate(action.effects))):
if effect.literal.negated:
del action.effects[index]
sas_task = pddl_to_sas(task)
dump_statistics(sas_task)
with timers.timing("Writing output"):
with open("output.sas", "w") as output_file:
sas_task.output(output_file)
print("Done! %s" % timer)
def main():
print("-------------POND Translator-----------")
args = parse_args()
timer = timers.Timer()
with timers.timing("Parsing", True):
task = pddl.open(task_filename=args.task, domain_filename=args.domain)
print();
print("Problem Filename = " + args.task);
print("Domain Filename = " + args.domain);
print();
with timers.timing("Normalizing task"):
normalize.normalize(task)
if args.generate_relaxed_task:
# Remove delete effects.
for action in task.actions:
for index, effect in reversed(list(enumerate(action.effects))):
if effect.literal.negated:
del action.effects[index]
sas_task = pddl_to_sas(task)
dump_statistics(sas_task)
if not sas_task is None:
with timers.timing("Writing output"):
with open("..\\webapps\\LunaPlanner\\translator_output\\output.sas", "w") as output_file:
sas_task.output(output_file)
print()
print("SAS file saved at: " + output_file.name)
print("Done! %s" % timer)
def main():
args = parse_args()
timer = timers.Timer()
with timers.timing("Parsing", True):
task = pddl.open(task_filename=args.task, domain_filename=args.domain)
with timers.timing("Normalizing task"):
normalize.normalize(task)
task.INVARIANT_TIME_LIMIT = int(args.inv_limit)
if args.generate_relaxed_task:
# Remove delete effects.
for action in task.actions:
for index, effect in reversed(list(enumerate(action.effects))):
if effect.literal.negated:
del action.effects[index]
sas_task = pddl_to_sas(task)
########### dump_statistics(sas_task).
with timers.timing("Writing output"):
with open(args.sas_name, "w") as output_file:
sas_task.output(output_file)
def translator(req):
task = pddl.open(req.problem,req.domain)
sas_task = pddl_to_sas(task)
output=sas_task.output()
del task
del sas_task
return translateResponse(str(output))
def extract(self, domain_path, instance_path):
features = self.default_features()
successful = False
try:
task = pddl.open(task_filename=instance_path, domain_filename=domain_path)
pddl_features = self.extract_features_from_pddl_task(task)
features.update(pddl_features)
# make sure at least one non-sentinel value, otherwise obviously not successful
for key,value in features.iteritems():
if value != self.sentinel_value:
successful = True
except Exception as e:
print "Exception running simple_pddl extraction: %s" % (str(e))
return successful,features
def extract(self, domain_path, instance_path):
features = self.default_features()
successful = False
try:
task = pddl.open(task_filename=instance_path,
domain_filename=domain_path)
pddl_features = self.extract_features_from_pddl_task(task)
features.update(pddl_features)
# make sure at least one non-sentinel value, otherwise obviously not successful
for key, value in features.iteritems():
if value != self.sentinel_value:
successful = True
except Exception as e:
print "Exception running simple_pddl extraction: %s" % (str(e))
return successful, features
def main():
options, args = parse_options()
check_python_version(options.force_old_python)
timer = timers.Timer()
with timers.timing("Parsing"):
task = pddl.open()
# EXPERIMENTAL!
# import psyco
# psyco.full()
sas_task = pddl_to_sas(task)
dump_statistics(sas_task)
with timers.timing("Writing output"):
with open("output.sas", "w") as output_file:
sas_task.output(output_file)
print("Done! %s" % timer)
def main():
options, args = parse_options()
check_python_version(options.force_old_python)
timer = timers.Timer()
with timers.timing("Parsing"):
task = pddl.open()
# EXPERIMENTAL!
# import psyco
# psyco.full()
sas_task = pddl_to_sas(task)
dump_statistics(sas_task)
with timers.timing("Writing output"):
with open("output.sas", "w") as output_file:
sas_task.output(output_file)
print("Done! %s" % timer)
def default(domain_file, problem_file, output_task):
parsing_timer = timers.Timer()
print("Domain: %s Problem: %s" % (domain_file, problem_file))
task = pddl.open(problem_file, domain_file)
relaxed_reachable, atoms, actions, axioms, reachable_action_params = explore(
task)
print("goal relaxed reachable: %s" % relaxed_reachable)
if not relaxed_reachable:
print("No weak plan exists")
sys.exit(2)
print("%d atoms" % len(atoms))
with timers.timing("Computing fact groups", block=True):
groups, mutex_groups, translation_key = fact_groups.compute_groups(
task,
atoms,
reachable_action_params,
partial_encoding=USE_PARTIAL_ENCODING)
index = 0
atom_table = {}
for atom in atoms:
atom.index = index
atom_table[atom.text()] = index
output_task.add_atom(atom.text())
index += 1
print("Invariants %d" % len(mutex_groups))
for group in mutex_groups:
if len(group) >= 2:
print("{%s}" % ", ".join(map(str, group)))
output_task.add_invariant(encode(group, atom_table))
#print( encode( group, atom_table ) )
print("Deterministic %d actions" % len(actions))
nd_actions = {}
for action in actions:
nd_action = PropositionalDetAction(action.name, action.cost)
nd_action.set_precondition(action.precondition, atom_table)
nd_action.add_effect(action.add_effects, action.del_effects,
atom_table)
nd_actions[nd_action.name] = nd_action
index = 0
for action in nd_actions.values():
output_task.add_action(action.name)
output_task.add_precondition(index, action.precondition)
text_prec = []
for p, v in action.precondition:
text_prec.append("%s=%s" % (output_task.get_atom_name(p), not v))
for eff in action.effects:
output_task.add_effect(index, eff)
text_eff = []
for p, v in eff:
text_eff.append("%s=%s" %
(output_task.get_atom_name(p), not v))
index += 1
output_task.set_domain_name(task.domain_name)
output_task.set_problem_name(task.task_name)
output_task.set_init(encode(task.init, atom_table))
output_task.set_goal(encode(task.goal, atom_table))
output_task.parsing_time = parsing_timer.report()
def default( domain_file, problem_file, output_task ) :
parsing_timer = timers.Timer()
print("Domain: %s Problem: %s"%(domain_file, problem_file) )
task = pddl.open( problem_file, domain_file)
normalize.normalize(task)
relaxed_reachable, atoms, actions, axioms, reachable_action_params = explore(task)
print("goal relaxed reachable: %s" % relaxed_reachable)
if not relaxed_reachable :
print("No plan exists")
sys.exit(2)
print("%d atoms" % len(atoms))
with timers.timing("Computing fact groups", block=True):
groups, mutex_groups, translation_key = fact_groups.compute_groups(
task, atoms, reachable_action_params,
partial_encoding=USE_PARTIAL_ENCODING)
index = 0
atom_table = {}
atom_names = [ atom.text() for atom in atoms ]
atom_names.sort()
for atom in atom_names :
atom_table[ atom ] = index
output_task.add_atom( atom )
index += 1
print("Deterministic %d actions" % len(actions))
nd_actions = {}
for action in actions :
#print( "action: %s cost: %d"%(action.name,action.cost) )
nd_action = PropositionalDetAction( action.name, action.cost )
nd_action.set_precondition( action.precondition, atom_table )
nd_action.add_effect( action.add_effects, action.del_effects, atom_table )
if len(nd_action.negated_conditions) > 0 :
output_task.notify_negated_conditions( nd_action.negated_conditions )
nd_actions[ nd_action.name ] = nd_action
output_task.create_negated_fluents()
for name, _ in nd_actions.iteritems() :
output_task.add_action( name )
index = 0
for action in nd_actions.values() :
output_task.add_precondition( index, action.precondition )
for eff in action.effects :
output_task.add_effect( index, eff )
#if len(action.cond_effs) != 0 :
# print action.name, len(action.cond_effs), "has conditional effects"
for cond, eff in action.cond_effs.iteritems() :
output_task.add_cond_effect( index, list(cond), eff )
output_task.set_cost( index, action.cost )
index += 1
# MRJ: Mutex groups processing needs to go after negations are compiled away
print("Invariants %d"%len(mutex_groups))
for group in mutex_groups :
if len(group) >= 2 :
#print("{%s}" % ", ".join(map(str, group)))
output_task.add_mutex_group( encode( group, atom_table ) )
#print( encode( group, atom_table ) )
output_task.set_domain_name( task.domain_name )
output_task.set_problem_name( task.task_name )
output_task.set_init( encode( task.init, atom_table ) )
output_task.set_goal( encode( task.goal, atom_table ) )
output_task.parsing_time = parsing_timer.report()
def default( domain_file, problem_file, output_task ) :
parsing_timer = timers.Timer()
print("Domain: %s Problem: %s"%(domain_file, problem_file) )
task = pddl.open( problem_file, domain_file)
relaxed_reachable, atoms, actions, axioms, reachable_action_params = explore(task)
print("goal relaxed reachable: %s" % relaxed_reachable)
if not relaxed_reachable :
print("No weak plan exists")
sys.exit(2)
print("%d atoms" % len(atoms))
with timers.timing("Computing fact groups", block=True):
groups, mutex_groups, translation_key = fact_groups.compute_groups(
task, atoms, reachable_action_params,
partial_encoding=USE_PARTIAL_ENCODING)
index = 0
atom_table = {}
for atom in atoms :
atom.index = index
atom_table[ atom.text() ] = index
output_task.add_atom( atom.text() )
index += 1
print("Invariants %d"%len(mutex_groups))
for group in mutex_groups :
if len(group) >= 2 :
print("{%s}" % ", ".join(map(str, group)))
output_task.add_invariant( encode( group, atom_table ) )
#print( encode( group, atom_table ) )
print("Deterministic %d actions" % len(actions))
nd_actions = {}
for action in actions :
nd_action = PropositionalDetAction( action.name, action.cost )
nd_action.set_precondition( action.precondition, atom_table )
nd_action.add_effect( action.add_effects, action.del_effects, atom_table )
nd_actions[ nd_action.name ] = nd_action
index = 0
for action in nd_actions.values() :
output_task.add_action( action.name )
output_task.add_precondition( index, action.precondition )
text_prec = []
for p, v in action.precondition :
text_prec.append( "%s=%s"%(output_task.get_atom_name( p ), not v) )
for eff in action.effects :
output_task.add_effect( index, eff )
text_eff = []
for p, v in eff :
text_eff.append( "%s=%s"%(output_task.get_atom_name( p ), not v) )
index += 1
output_task.set_domain_name( task.domain_name )
output_task.set_problem_name( task.task_name )
output_task.set_init( encode( task.init, atom_table ) )
output_task.set_goal( encode( task.goal, atom_table ) )
output_task.parsing_time = parsing_timer.report()
def default(domain_file, problem_file, output_task):
parsing_timer = timers.Timer()
print("Domain: %s Problem: %s" % (domain_file, problem_file))
task = pddl.open(problem_file, domain_file)
normalize.normalize(task)
relaxed_reachable, atoms, actions, axioms, reachable_action_params = explore(
task)
print("goal relaxed reachable: %s" % relaxed_reachable)
if not relaxed_reachable:
print("No plan exists")
sys.exit(2)
print("%d atoms" % len(atoms))
with timers.timing("Computing fact groups", block=True):
groups, mutex_groups, translation_key = fact_groups.compute_groups(
task,
atoms,
reachable_action_params,
partial_encoding=USE_PARTIAL_ENCODING)
index = 0
atom_table = {}
atom_names = [atom.text() for atom in atoms]
atom_names.sort()
for atom in atom_names:
atom_table[atom] = index
output_task.add_atom(atom)
index += 1
print("Deterministic %d actions" % len(actions))
nd_actions = {}
for action in actions:
#print( "action: %s cost: %d"%(action.name,action.cost) )
nd_action = PropositionalDetAction(action.name, action.cost)
nd_action.set_precondition(action.precondition, atom_table)
nd_action.add_effect(action.add_effects, action.del_effects,
atom_table)
if len(nd_action.negated_conditions) > 0:
output_task.notify_negated_conditions(nd_action.negated_conditions)
nd_actions[nd_action.name] = nd_action
output_task.create_negated_fluents()
for name, _ in nd_actions.iteritems():
output_task.add_action(name)
index = 0
for action in nd_actions.values():
output_task.add_precondition(index, action.precondition)
for eff in action.effects:
output_task.add_effect(index, eff)
#if len(action.cond_effs) != 0 :
# print action.name, len(action.cond_effs), "has conditional effects"
for cond, eff in action.cond_effs.iteritems():
output_task.add_cond_effect(index, list(cond), eff)
output_task.set_cost(index, action.cost)
index += 1
# MRJ: Mutex groups processing needs to go after negations are compiled away
print("Invariants %d" % len(mutex_groups))
for group in mutex_groups:
if len(group) >= 2:
#print("{%s}" % ", ".join(map(str, group)))
output_task.add_mutex_group(encode(group, atom_table))
#print( encode( group, atom_table ) )
output_task.set_domain_name(task.domain_name)
output_task.set_problem_name(task.task_name)
output_task.set_init(encode(task.init, atom_table))
output_task.set_goal(encode(task.goal, atom_table))
output_task.parsing_time = parsing_timer.report()
def fodet(domain_file, problem_file, output_task):
parsing_timer = timers.Timer()
print("Domain: %s Problem: %s" % (domain_file, problem_file))
task = pddl.open(problem_file, domain_file)
normalize.normalize(task)
relaxed_reachable, atoms, actions, axioms, reachable_action_params = explore(
task)
print("goal relaxed reachable: %s" % relaxed_reachable)
if not relaxed_reachable:
print("No plan exists")
sys.exit(2)
print("%d atoms" % len(atoms))
with timers.timing("Computing fact groups", block=True):
groups, mutex_groups, translation_key = fact_groups.compute_groups(
task,
atoms,
reachable_action_params,
partial_encoding=USE_PARTIAL_ENCODING)
index = 0
atom_table = {}
atom_names = [atom.text() for atom in atoms]
atom_names.sort()
for atom in atom_names:
atom_table[atom] = index
output_task.add_atom(atom)
index += 1
print("Axioms %d" % len(axioms))
for axiom in axioms:
axiom.dump()
output_task.add_axiom(encode(axiom.condition, atom_table),
encode([axiom.effect], atom_table))
print("Deterministic %d actions" % len(actions))
nd_actions = {}
for action in actions:
#print( "action: %s cost: %d"%(action.name,action.cost) )
nd_action = PropositionalDetAction(action.name, action.cost)
nd_action.set_precondition(action.precondition, atom_table)
nd_action.add_effect(action.add_effects, action.del_effects,
atom_table)
nd_actions[nd_action.name] = nd_action
for name, _ in nd_actions.iteritems():
output_task.add_action(name)
index = 0
for action in nd_actions.values():
output_task.add_precondition(index, action.precondition)
for eff in action.effects:
output_task.add_effect(index, eff)
#if len(action.cond_effs) != 0 :
# print action.name, len(action.cond_effs), "has conditional effects"
for cond, eff in action.cond_effs.iteritems():
output_task.add_cond_effect(index, list(cond), eff)
output_task.set_cost(index, action.cost)
index += 1
output_task.set_domain_name(task.domain_name)
output_task.set_problem_name(task.task_name)
output_task.set_init(encode(task.init, atom_table))
output_task.set_goal(encode(task.goal, atom_table))
output_task.parsing_time = parsing_timer.report()
def numeric(domain_file, problem_file, output_task):
import liblapkt
print("Domain: %s Problem: %s" % (domain_file, problem_file))
task = pddl.open(problem_file, domain_file)
normalize.normalize(task)
relaxed_reachable, atoms, actions, axioms, reachable_action_params = explore(
task)
atom_table = dict()
# atoms
for atom in atoms:
if atom.text() not in atom_table:
atom_table[atom.text()] = TableItem(len(atom_table), atom)
output_task.add_atom(atom.text())
init_atoms = [
x for x in task.init
if (not hasattr(x, 'predicate')) or x.predicate != '='
]
function_table = dict()
# functions
# all ground functions should have some value assigned in init
# unlike logical fluents, which are false if are not specified
for func in (x for x in task.init
if isinstance(x, pddl.f_expression.Assign)):
if isinstance(func.fluent, pddl.f_expression.PrimitiveNumericExpression
) and func.fluent.symbol == 'total-cost':
continue
atom = pddl.Atom(func.fluent.symbol, func.fluent.args)
function_table[atom.text()] = TableItem(len(function_table), atom,
func.expression.value)
output_task.add_function(atom.text())
negated_set = set()
action_data = []
# actions
for instance in actions:
precs, cmp, new_atoms = convert_precondition(instance.precondition,
atom_table)
for new_atom in new_atoms:
output_task.add_atom(new_atom)
negated_condistions = [
x[0] for x in precs if (x[1] and x[0] not in negated_set)
]
for n in negated_condistions:
if n not in negated_set:
negated_set.add(n)
print("negated: " + str(n))
output_task.notify_negated_conditions([n])
effs = convert_effect(instance.add_effects, instance.del_effects,
atom_table)
num_effs = convert_num_effect(instance.numeric_effects, function_table)
action_data.append(
NumDetAction(instance.name, instance.cost, precs, effs, num_effs,
cmp))
output_task.create_negated_fluents()
# numerical conditions
cmp_map = {
'>': liblapkt.CompareType.more,
'>=': liblapkt.CompareType.more_equal
}
for (index, action) in enumerate(action_data):
# todo: add comparision objects
output_task.add_action(action.name)
output_task.define_action(index, action.preconditions, action.effects,
action.numeric_effects)
output_task.set_cost(index, action.cost)
# add comparision objects to output task
for (idx, cmp) in action.comparison.items():
expr = convert_expression(cmp.parts[0], function_table)
output_task.add_comparison(idx, cmp_map[cmp.comparator], expr)
# adding init and goal
num_list = []
for item in function_table.values():
num_list.append((item.index, item.value))
fluent_list = []
for item in init_atoms:
if isinstance(item, (pddl.Atom, pddl.NegatedAtom)):
try:
fluent_list.append(
(atom_table[item.text()].index, item.negated))
except KeyError as e:
# ignore since some
pass
assert len(num_list) == len(function_table)
output_task.set_init_num(fluent_list, num_list)
goal = [(x[0].index, x[1]) for x in encode(task.goal, atom_table)]
output_task.set_goal(goal)
# process metric
if task.metric_expression:
metric_expression = convert_expression(task.metric_expression,
function_table)
output_task.set_metric_expression(metric_expression)
output_task.set_add_cost(False)
return result
def popped_elements(self):
return queue.queue[:self.queue_pos]
def compute_model(prog):
rules = convert_rules(prog)
unifier = Unifier(rules)
# unifier.dump()
queue = Queue([fact.atom for fact in prog.facts])
print "Starting instantiation [%d rules]..." % len(rules)
while queue:
next_atom = queue.pop()
matches = unifier.unify(next_atom)
for rule, cond_index in matches:
rule.update_index(next_atom, cond_index)
rule.fire(next_atom, cond_index, queue.push)
return queue.queue
if __name__ == "__main__":
import pddl_to_prolog
print "Parsing..."
task = pddl.open()
print "Writing rules..."
prog = pddl_to_prolog.translate(task)
print "Computing model..."
for atom in compute_model(prog):
print atom
def fodet( domain_file, problem_file, output_task ) :
parsing_timer = timers.Timer()
print("Domain: %s Problem: %s"%(domain_file, problem_file) )
task = pddl.open( problem_file, domain_file)
normalize.normalize(task)
relaxed_reachable, atoms, actions, axioms, reachable_action_params = explore(task)
print("goal relaxed reachable: %s" % relaxed_reachable)
if not relaxed_reachable :
print("No plan exists")
sys.exit(2)
print("%d atoms" % len(atoms))
with timers.timing("Computing fact groups", block=True):
groups, mutex_groups, translation_key = fact_groups.compute_groups(
task, atoms, reachable_action_params,
partial_encoding=USE_PARTIAL_ENCODING)
index = 0
atom_table = {}
atom_names = [ atom.text() for atom in atoms ]
atom_names.sort()
for atom in atom_names :
atom_table[ atom ] = index
output_task.add_atom( atom )
index += 1
print("Axioms %d"%len(axioms))
for axiom in axioms:
axiom.dump()
output_task.add_axiom( encode( axiom.condition, atom_table), encode( [ axiom.effect ], atom_table ))
print("Deterministic %d actions" % len(actions))
nd_actions = {}
for action in actions :
#print( "action: %s cost: %d"%(action.name,action.cost) )
nd_action = PropositionalDetAction( action.name, action.cost )
nd_action.set_precondition( action.precondition, atom_table )
nd_action.add_effect( action.add_effects, action.del_effects, atom_table )
nd_actions[ nd_action.name ] = nd_action
for name, _ in nd_actions.iteritems() :
output_task.add_action( name )
index = 0
for action in nd_actions.values() :
output_task.add_precondition( index, action.precondition )
for eff in action.effects :
output_task.add_effect( index, eff )
#if len(action.cond_effs) != 0 :
# print action.name, len(action.cond_effs), "has conditional effects"
for cond, eff in action.cond_effs.iteritems() :
output_task.add_cond_effect( index, list(cond), eff )
output_task.set_cost( index, action.cost )
index += 1
output_task.set_domain_name( task.domain_name )
output_task.set_problem_name( task.task_name )
output_task.set_init( encode( task.init, atom_table ) )
output_task.set_goal( encode( task.goal, atom_table ) )
output_task.parsing_time = parsing_timer.report()
if not isinstance(atom,pddl.FunctionAssignment):
for invariant in predicate_to_invariants.get(atom.predicate, ()):
group_key = (invariant, tuple(invariant.get_parameters(atom)))
if group_key not in nonempty_groups:
nonempty_groups.add(group_key)
else:
overcrowded_groups.add(group_key)
useful_groups = nonempty_groups - overcrowded_groups
for (invariant, parameters) in useful_groups:
yield [part.instantiate(parameters) for part in invariant.parts]
def get_groups(task, safe=True, reachable_action_params=None):
with timers.timing("Finding invariants"):
invariants = list(find_invariants(task, safe, reachable_action_params))
invariants = sorted(invariants)
with timers.timing("Checking invariant weight"):
result = list(useful_groups(invariants, task.init))
return result
if __name__ == "__main__":
import pddl
print "Parsing..."
task = pddl.open()
print "Finding invariants..."
for invariant in find_invariants(task):
print invariant
print "Finding fact groups..."
groups = get_groups(task)
for group in groups:
print "[%s]" % ", ".join(map(str, group))
(task_filename, domain_filename, output_dir) = ('', '', '')
if len(sys.argv) == 2:
task_filename = sys.argv[1]
elif len(sys.argv) == 3:
(task_filename, domain_filename) = sys.argv[1:]
elif len(sys.argv) == 4:
(task_filename, domain_filename, output_dir) = sys.argv[1:]
else:
print(
"Needs 1-3 arguments: task_filename [, domain_filename [, output_dir]]"
)
sys.exit(1)
print("Parsing...")
task = pddl.open(task_filename, domain_filename)
if task.domain_name in ["protocol", "rover"]:
# This is, of course, a HACK HACK HACK!
# The real issue is that ALLOW_CONFLICTING_EFFECTS = True
# is actually the correct semantics, but then we don't get to filter
# out operators that are impossible to apply due to mutexes between
# different SAS+ variables. For example,
# ALLOW_CONFLICTING_EFFECTS = True does not filter on(a,a) in
# blocksworld/4-0.
ALLOW_CONFLICTING_EFFECTS = True
# EXPERIMENTAL!
# import psyco
# psyco.full()
sas_task = pddl_to_sas(task)
