def remove_duration_variable(task):
def recurse(condition, act, time, duration, pnes):
if isinstance(condition, pddl.FunctionComparison):
parts = [
exp.remove_duration_variable(act, time, duration, pnes)
for exp in condition.parts
]
return condition.__class__(condition.comparator, parts)
# return pddl.FunctionComparison(condition.comparator,parts)
else:
new_parts = [
recurse(part, act, time, duration, pnes)
for part in condition.parts
]
return condition.change_parts(new_parts)
for act in task.durative_actions:
assert len(act.duration[1]) == 0, "at end durations are not supported"
assert len(act.duration[0]) == 1 and act.duration[0][0][0] == "="
duration = act.duration[0][0][1]
duration_functions = []
# remove from action conditions
condition = []
for time, cond in enumerate(act.condition):
condition.append(
recurse(cond, act, time, duration, duration_functions))
act.condition = condition
for time in range(2):
for eff in act.effects[time]:
# remove from effect condition
condition = []
for eff_time, cond in enumerate(eff.condition):
condition.append(
recurse(cond, act, eff_time, duration,
duration_functions))
eff.condition = condition
# remove from effect
if isinstance(eff.peffect, pddl.FunctionAssignment):
assign = eff.peffect
assign.expression = assign.expression.remove_duration_variable(
act, time, duration, duration_functions)
for pne in duration_functions:
assign = pddl.Assign(pne, duration)
condition = [pddl.Truth(), pddl.Truth(), pddl.Truth()]
effect = pddl.Effect([], condition, assign)
act.effects[0].append(effect)
task.function_symbols[pne.symbol] = "number"
def add_effect(tmp_effect, result):
"""tmp_effect has the following structure:
[ConjunctiveEffect] [UniversalEffect] [ConditionalEffect] SimpleEffect."""
if isinstance(tmp_effect, pddl.ConjunctiveEffect):
for effect in tmp_effect.effects:
add_effect(effect, result)
return
else:
parameters = []
condition = pddl.Truth()
if isinstance(tmp_effect, pddl.UniversalEffect):
parameters = tmp_effect.parameters
if isinstance(tmp_effect.effect, pddl.ConditionalEffect):
condition = tmp_effect.effect.condition
assert isinstance(tmp_effect.effect.effect, pddl.SimpleEffect)
effect = tmp_effect.effect.effect.effect
else:
assert isinstance(tmp_effect.effect, pddl.SimpleEffect)
effect = tmp_effect.effect.effect
elif isinstance(tmp_effect, pddl.ConditionalEffect):
condition = tmp_effect.condition
assert isinstance(tmp_effect.effect, pddl.SimpleEffect)
effect = tmp_effect.effect.effect
else:
assert isinstance(tmp_effect, pddl.SimpleEffect)
effect = tmp_effect.effect
assert isinstance(effect, pddl.Literal)
# Check for contradictory effects
condition = condition.simplified()
new_effect = pddl.Effect(parameters, condition, effect)
contradiction = pddl.Effect(parameters, condition, effect.negate())
### REMOVED CONTRADICTION CHECK
result.append(new_effect)
def convert_condition(condition):
import pddl
class_name = condition.__class__.__name__
if class_name in ('Truth', 'FunctionComparison'):
# TODO: currently ignoring numeric conditions
return pddl.Truth()
elif class_name == 'Atom':
return pddl.Atom(condition.predicate, convert_args(condition.args))
elif class_name == 'NegatedAtom':
return pddl.NegatedAtom(condition.predicate,
convert_args(condition.args))
elif class_name == 'Conjunction':
return pddl.conditions.Conjunction(
list(map(convert_condition, condition.parts)))
elif class_name == 'Disjunction':
return pddl.Disjunction(list(map(convert_condition, condition.parts)))
elif class_name == 'ExistentialCondition':
return pddl.ExistentialCondition(
convert_parameters(condition.parameters),
list(map(convert_condition, condition.parts)))
elif class_name == 'UniversalCondition':
return pddl.UniversalCondition(
convert_parameters(condition.parameters),
list(map(convert_condition, condition.parts)))
raise NotImplementedError(class_name)
def get_problem(evaluations, goal_exp, domain, unit_costs=False):
objects = objects_from_evaluations(evaluations)
typed_objects = list(
{make_object(pddl_from_object(obj))
for obj in objects} - set(domain.constants))
# TODO: this doesn't include =
init = [
fd_from_evaluation(e) for e in evaluations if not is_negated_atom(e)
]
goal = pddl.Truth() if goal_exp is None else parse_goal(goal_exp, domain)
problem_pddl = None
if USE_FORBID:
problem_pddl = get_problem_pddl(evaluations,
goal_exp,
domain.pddl,
temporal=False)
write_pddl(domain.pddl, problem_pddl)
return Problem(task_name=domain.name,
task_domain_name=domain.name,
objects=sorted(typed_objects, key=lambda o: o.name),
task_requirements=pddl.tasks.Requirements([]),
init=init,
goal=goal,
use_metric=not unit_costs,
pddl=problem_pddl)
def get_stream_action(result, name, unit_cost, effect_scale=1):
#from pddl_parser.parsing_functions import parse_action
import pddl
parameters = []
preconditions = [
fd_from_fact(fact) for fact in result.instance.get_domain()
]
precondition = pddl.Conjunction(preconditions)
effects = [
pddl.Effect(parameters=[],
condition=pddl.Truth(),
literal=fd_from_fact(fact))
for fact in result.get_certified()
]
effort = 1 if unit_cost else result.instance.get_effort()
if effort == INF:
return None
fluent = pddl.PrimitiveNumericExpression(symbol=TOTAL_COST, args=[])
expression = pddl.NumericConstant(int_ceil(effect_scale *
effort)) # Integer
cost = pddl.Increase(fluent=fluent,
expression=expression) # Can also be None
return pddl.Action(name=name,
parameters=parameters,
num_external_parameters=len(parameters),
precondition=precondition,
effects=effects,
cost=cost)
def get_necessary_axioms(instance, axioms, negative_from_name):
import pddl
axioms_from_name = get_derived_predicates(axioms)
atom_queue = []
processed_atoms = set()
def add_literals(literals):
for literal in literals:
atom = literal.positive()
if atom not in processed_atoms:
atom_queue.append(literal.positive())
processed_atoms.add(atom)
add_literals(instance.precondition)
for (cond, _) in (instance.add_effects + instance.del_effects):
add_literals(cond)
axiom_from_action = {}
partial_instantiations = set()
while atom_queue:
literal = atom_queue.pop()
for axiom in axioms_from_name[literal.predicate]:
derived_parameters = axiom.parameters[:axiom.
num_external_parameters]
var_mapping = {
p.name: a
for p, a in zip(derived_parameters, literal.args)
if a[0] != '?'
}
key = (axiom, frozenset(var_mapping.items()))
if key in partial_instantiations:
continue
partial_instantiations.add(key)
parts = []
for literal in get_literals(axiom.condition):
if literal.predicate in negative_from_name:
continue
parts.append(literal.rename_variables(var_mapping))
# new_condition = axiom.condition.uniquify_variables(None, var_mapping)
effect_args = [
var_mapping.get(a.name, a.name) for a in derived_parameters
]
effect = pddl.Effect([], pddl.Truth(),
pddl.conditions.Atom(axiom.name, effect_args))
free_parameters = [
p for p in axiom.parameters if p.name not in var_mapping
]
new_action = pddl.Action(axiom.name, free_parameters, 0,
pddl.Conjunction(parts), [effect], None)
# Creating actions so I can partially instantiate (impossible with axioms)
axiom_from_action[new_action] = (axiom, var_mapping)
add_literals(parts)
return axiom_from_action
def get_necessary_axioms(conditions, axioms, negative_from_name):
if not conditions or not axioms:
return {}
axioms_from_name = get_derived_predicates(axioms)
atom_queue = []
processed_atoms = set()
def add_literals(literals):
for lit in literals:
atom = lit.positive()
if atom not in processed_atoms:
atom_queue.append(
atom) # Previously was lit.positive() for some reason?
processed_atoms.add(atom)
import pddl
add_literals(conditions)
axiom_from_action = {}
partial_instantiations = set()
while atom_queue:
literal = atom_queue.pop()
for axiom in axioms_from_name[literal.predicate]:
derived_parameters = axiom.parameters[:axiom.
num_external_parameters]
var_mapping = {
p.name: a
for p, a in zip(derived_parameters, literal.args)
if not is_parameter(a)
}
key = (axiom, frozenset(var_mapping.items()))
if key in partial_instantiations:
continue
partial_instantiations.add(key)
parts = [
l.rename_variables(var_mapping)
for l in get_literals(axiom.condition)
if l.predicate not in negative_from_name
] # Assumes a conjunction?
# new_condition = axiom.condition.uniquify_variables(None, var_mapping)
effect_args = [
var_mapping.get(a.name, a.name) for a in derived_parameters
]
effect = pddl.Effect([], pddl.Truth(),
pddl.conditions.Atom(axiom.name, effect_args))
free_parameters = [
p for p in axiom.parameters if p.name not in var_mapping
]
new_action = pddl.Action(axiom.name, free_parameters,
len(free_parameters),
pddl.Conjunction(parts), [effect], None)
# Creating actions so I can partially instantiate (impossible with axioms)
axiom_from_action[new_action] = (axiom, var_mapping)
add_literals(parts)
return axiom_from_action
def add_effect(tmp_effect, result):
"""tmp_effect has the following structure:
[ConjunctiveEffect] [UniversalEffect] [ConditionalEffect] SimpleEffect."""
if isinstance(tmp_effect, pddl.ConjunctiveEffect):
for effect in tmp_effect.effects:
add_effect(effect, result)
return
else:
parameters = []
condition = pddl.Truth()
if isinstance(tmp_effect, pddl.UniversalEffect):
parameters = tmp_effect.parameters
if isinstance(tmp_effect.effect, pddl.ConditionalEffect):
condition = tmp_effect.effect.condition
assert isinstance(tmp_effect.effect.effect, pddl.SimpleEffect)
effect = tmp_effect.effect.effect.effect
else:
assert isinstance(tmp_effect.effect, pddl.SimpleEffect)
effect = tmp_effect.effect.effect
elif isinstance(tmp_effect, pddl.ConditionalEffect):
condition = tmp_effect.condition
assert isinstance(tmp_effect.effect, pddl.SimpleEffect)
effect = tmp_effect.effect.effect
else:
assert isinstance(tmp_effect, pddl.SimpleEffect)
effect = tmp_effect.effect
if effect is None:
# Nothing to add
return
assert isinstance(effect, pddl.Literal)
# Check for contradictory effects
condition = condition.simplified()
new_effect = pddl.Effect(parameters, condition, effect)
contradiction = pddl.Effect(parameters, condition, effect.negate())
if not contradiction in result:
result.append(new_effect)
else:
# We use add-after-delete semantics, keep positive effect
if isinstance(contradiction.literal, pddl.NegatedAtom):
result.remove(contradiction)
result.append(new_effect)
def compile_to_exogenous_actions(evaluations, domain, streams):
import pddl
# TODO: automatically derive fluents
# TODO: version of this that operates on fluents of length one?
# TODO: better instantiation when have full parameters
# TODO: conversion from stream cost to real cost units?
# TODO: any predicates derived would need to be replaced as well
fluent_predicates = get_fluents(domain)
domain_predicates = {get_prefix(a) for s in streams for a in s.domain}
if not (domain_predicates & fluent_predicates):
return
certified_predicates = {get_prefix(a) for s in streams for a in s.certified}
future_map = {p: 'f-{}'.format(p) for p in certified_predicates}
augment_evaluations(evaluations, future_map)
rename_future = lambda a: rename_atom(a, future_map)
for stream in list(streams):
if not isinstance(stream, Stream):
raise NotImplementedError(stream)
# TODO: could also just have conditions asserting that one of the fluent conditions fails
streams.append(create_static_stream(stream, evaluations, fluent_predicates, rename_future))
stream_atom = streams[-1].certified[0]
parameters = [pddl.TypedObject(p, 'object') for p in get_args(stream_atom)]
# TODO: add to predicates as well?
domain.predicate_dict[get_prefix(stream_atom)] = pddl.Predicate(get_prefix(stream_atom), parameters)
precondition = pddl.Conjunction(tuple(map(fd_from_fact, (stream_atom,) + tuple(stream.domain))))
effects = [pddl.Effect(parameters=[], condition=pddl.Truth(),
literal=fd_from_fact(fact)) for fact in stream.certified]
effort = 1 # TODO: use stream info
#effort = 1 if unit_cost else result.instance.get_effort()
#if effort == INF:
# continue
fluent = pddl.PrimitiveNumericExpression(symbol=TOTAL_COST, args=[])
expression = pddl.NumericConstant(int_ceil(effort)) # Integer
cost = pddl.Increase(fluent=fluent, expression=expression) # Can also be None
domain.actions.append(pddl.Action(name='call-{}'.format(stream.name),
parameters=parameters,
num_external_parameters=len(parameters),
precondition=precondition, effects=effects, cost=cost))
stream.certified = tuple(set(stream.certified) |
set(map(rename_future, stream.certified)))
def make_effects(effects):
return [
pddl.Effect(parameters=[],
condition=pddl.Truth(),
literal=fd_from_fact(fact)) for fact in effects
]
def parse_task_pddl(task_pddl, type_dict, predicate_dict):
iterator = iter(task_pddl)
define_tag = next(iterator)
assert define_tag == "define"
problem_line = next(iterator)
assert problem_line[0] == "problem" and len(problem_line) == 2
yield problem_line[1]
domain_line = next(iterator)
assert domain_line[0] == ":domain" and len(domain_line) == 2
yield domain_line[1]
requirements_opt = next(iterator)
if requirements_opt[0] == ":requirements":
requirements = requirements_opt[1:]
objects_opt = next(iterator)
else:
requirements = []
objects_opt = requirements_opt
yield pddl.Requirements(requirements)
if objects_opt[0] == ":objects":
yield parse_typed_list(objects_opt[1:])
init = next(iterator)
else:
yield []
init = objects_opt
assert init[0] == ":init"
initial = []
initial_true = set()
initial_false = set()
initial_assignments = dict()
for fact in init[1:]:
if fact[0] == "=":
try:
assignment = parse_assignment(fact)
except ValueError as e:
raise SystemExit("Error in initial state specification\n" +
"Reason: %s." % e)
if not isinstance(assignment.expression, pddl.NumericConstant):
raise SystemExit("Illegal assignment in initial state " +
"specification:\n%s" % assignment)
if assignment.fluent in initial_assignments:
prev = initial_assignments[assignment.fluent]
if assignment.expression == prev.expression:
print("Warning: %s is specified twice" % assignment,
"in initial state specification")
else:
raise SystemExit("Error in initial state specification\n" +
"Reason: conflicting assignment for " +
"%s." % assignment.fluent)
else:
initial_assignments[assignment.fluent] = assignment
initial.append(assignment)
elif fact[0] == "not":
atom = pddl.Atom(fact[1][0], fact[1][1:])
check_atom_consistency(atom, initial_false, initial_true, False)
initial_false.add(atom)
else:
atom = pddl.Atom(fact[0], fact[1:])
check_atom_consistency(atom, initial_true, initial_false)
initial_true.add(atom)
initial.extend(initial_true)
yield initial
goal = next(iterator)
goal_cond = pddl.Truth()
if goal[0] == ":goal":
if len(goal) == 2:
goal_cond = parse_condition(goal[1], type_dict, predicate_dict)
utility = next(iterator)
else:
utility = goal
yield goal_cond
assert utility[0] == ":utility"
utility_list = []
for fact in utility[1:]:
assert fact[0] == "="
utility_atom = pddl.Atom(fact[1][0], fact[1][1:])
utility_value = fact[2]
utility_list.append((utility_atom, utility_value))
yield utility_list
bound = next(iterator)
assert bound[0] == ":bound" and len(bound) == 2
yield bound[1]
use_metric = False
for entry in iterator:
if entry[0] == ":use-cost-metric":
use_metric = True
# for entry in iterator:
# if entry[0] == ":metric":
# if entry[1]=="maximize" and entry[2][0] == "total-utility":
# use_metric = True
# else:
# assert False, "Unknown metric."
yield use_metric
for entry in iterator:
assert False, entry
def add_effect(tmp_effect, result): # adds effect from tmp_effect to result
"""tmp_effect has the following structure:
[ConjunctiveEffect] [UniversalEffect] [ConditionalEffect] SimpleEffect."""
if isinstance(tmp_effect, pddl.ConjunctiveEffect):
for effect in tmp_effect.effects:
add_effect(effect, result)
return
else:
parameters = []
condition = pddl.Truth()
if isinstance(tmp_effect, pddl.UniversalEffect):
# print ("universal effect: ", tmp_effect)
parameters = tmp_effect.parameters
if isinstance(tmp_effect.effect, pddl.ConditionalEffect):
condition = tmp_effect.effect.condition
assert isinstance(tmp_effect.effect.effect, pddl.SimpleEffect)\
or isinstance(tmp_effect.effect.effect, pddl.NumericEffect)
effect = tmp_effect.effect.effect.effect
else:
assert isinstance(tmp_effect.effect, pddl.SimpleEffect)\
or isinstance(tmp_effect.effect, pddl.NumericEffect), "Effect is not primitive but %s" % tmp_effect.effect.effect.__class__
effect = tmp_effect.effect.effect
elif isinstance(tmp_effect, pddl.ConditionalEffect):
# print ("conditional effect: ", tmp_effect)
condition = tmp_effect.condition
assert isinstance(tmp_effect.effect, pddl.SimpleEffect)
effect = tmp_effect.effect.effect
elif isinstance(tmp_effect, pddl.SimpleEffect):
# print ("simple effect: ", tmp_effect)
effect = tmp_effect.effect
assert isinstance(effect, pddl.Literal)
else:
# print ("numeric effect: ", tmp_effect)
effect = tmp_effect.effect
assert isinstance(effect, pddl.FunctionAssignment)
# Check for contradictory effects
condition = condition.simplified()
new_effect = pddl.Effect(parameters, condition, effect)
if isinstance(effect, pddl.FunctionAssignment):
# The check for multiple effects on the same variable loops through all effects over and over again.
# If this raises performance issues it has to be reconsidered
# print("Check for same effect on numeric variable %s" % effect.fluent)
conflict = False
for other_effect in result:
if isinstance(other_effect, pddl.FunctionAssignment):
# print("comparing to %s -> Conflict? %s" % (other_effect.fluent, (effect.fluent == other_effect.fluent)))
if (effect.fluent == other_effect.fluent):
conflict = True
break
if conflict:
print(
"Warning, multiple effects on numeric variable %s, ignoring %s"
% (other_effect.fluent, effect.fluent))
else:
result.append(new_effect)
else:
assert isinstance(effect, pddl.Literal)
contradiction = pddl.Effect(parameters, condition, effect.negate())
if not contradiction in result:
result.append(new_effect)
else:
# We use add-after-delete semantics, keep positive effect
if isinstance(contradiction.peffect, pddl.NegatedAtom):
result.remove(contradiction)
result.append(new_effect)
