def extract_order(evaluations, preimage):
stream_results = set()
processed = set(map(fact_from_fd, preimage))
queue = deque(processed)
while queue:
fact = queue.popleft()
result = evaluations[evaluation_from_fact(fact)]
if result is None:
continue
stream_results.add(result)
for fact2 in result.instance.get_domain():
if fact2 not in processed:
queue.append(fact2)
orders = set()
for result in stream_results:
for fact in result.instance.get_domain():
result2 = evaluations[evaluation_from_fact(fact)]
if result2 is not None:
orders.add((result2, result))
ordered_results = []
for result in topological_sort(stream_results, orders):
if isinstance(result, SynthStreamResult):
ordered_results.extend(result.decompose())
else:
ordered_results.append(result)
return ordered_results
def shorten_stream_plan(evaluations, stream_plan, target_facts):
all_subgoals = set(target_facts) | set(
flatten(r.instance.get_domain() for r in stream_plan))
evaluation_subgoals = set(
filter(evaluations.__contains__, map(evaluation_from_fact,
all_subgoals)))
open_subgoals = set(
filter(lambda f: evaluation_from_fact(f) not in evaluations,
all_subgoals))
results_from_fact = {}
for result in stream_plan:
for fact in result.get_certified():
results_from_fact.setdefault(fact, []).append(result)
for removed_result in reversed(stream_plan): # TODO: only do in order?
certified_subgoals = open_subgoals & set(
removed_result.get_certified())
if not certified_subgoals: # Could combine with following
new_stream_plan = stream_plan[:]
new_stream_plan.remove(removed_result)
return new_stream_plan
if all(2 <= len(results_from_fact[fact])
for fact in certified_subgoals):
node_from_atom = get_achieving_streams(
evaluation_subgoals,
set(stream_plan) - {removed_result})
if all(fact in node_from_atom for fact in target_facts):
new_stream_plan = []
extract_stream_plan(node_from_atom, target_facts,
new_stream_plan)
return new_stream_plan
return None
def create_visualizations(evaluations, stream_plan, iteration):
# TODO: place it in the temp_dir?
# TODO: decompose any joint streams
for result in stream_plan:
create_synthesizer_visualizations(result, iteration)
filename = ITERATION_TEMPLATE.format(iteration)
# visualize_stream_plan(stream_plan, path)
constraints = set() # TODO: approximates needed facts using produced ones
for stream in stream_plan:
constraints.update(
filter(lambda f: evaluation_from_fact(f) not in evaluations,
stream.get_certified()))
print('Constraints:', str_from_object(constraints))
visualize_constraints(constraints,
os.path.join(CONSTRAINT_NETWORK_DIR, filename))
from pddlstream.retired.synthesizer import decompose_stream_plan
decomposed_plan = decompose_stream_plan(stream_plan)
if len(decomposed_plan) != len(stream_plan):
visualize_stream_plan(decompose_stream_plan(stream_plan),
os.path.join(STREAM_PLAN_DIR, filename))
#visualize_stream_plan_bipartite(stream_plan, os.path.join(STREAM_PLAN_DIR, 'fused_' + filename))
visualize_stream_plan(stream_plan,
os.path.join(STREAM_PLAN_DIR, 'fused_' + filename))
def replan_with_optimizers(evaluations, external_plan, domain, optimizers):
# TODO: return multiple plans?
# TODO: can instead have multiple goal binding combinations
# TODO: can replan using samplers as well
if not is_plan(external_plan):
return None
optimizers = list(
filter(lambda s: isinstance(s, ComponentStream), optimizers))
if not optimizers:
return None
stream_plan, function_plan = partition_external_plan(external_plan)
free_parameters = {o for r in stream_plan for o in r.output_objects}
#free_parameters = {o for r in stream_plan for o in r.output_objects if isinstance(o, OptimisticObject)}
initial_evaluations = {
e: n
for e, n in evaluations.items() if n.result == INIT_EVALUATION
}
#initial_evaluations = evaluations
goal_facts = set()
for result in stream_plan:
goal_facts.update(
filter(
lambda f: evaluation_from_fact(f) not in initial_evaluations,
result.get_certified()))
visited_facts = set()
new_results = []
for fact in goal_facts:
retrace_instantiation(fact, optimizers, initial_evaluations,
free_parameters, visited_facts, new_results)
# TODO: ensure correct ordering
new_results = list(
filter(lambda r: isinstance(r, ComponentStream), new_results))
#from pddlstream.algorithms.scheduling.recover_streams import get_achieving_streams, extract_stream_plan
#node_from_atom = get_achieving_streams(evaluations, stream_results) # TODO: make these lower effort
#extract_stream_plan(node_from_atom, target_facts, stream_plan)
optimizer_results = []
for optimizer in {get_optimizer(r)
for r in new_results
}: # None is like a unique optimizer:
relevant_results = [
r for r in new_results if get_optimizer(r) == optimizer
]
optimizer_results.extend(
combine_optimizer_plan(relevant_results, function_plan))
#print(str_from_object(set(map(fact_from_evaluation, evaluations))))
#print(str_from_object(set(goal_facts)))
# TODO: can do the flexibly sized optimizers search
from pddlstream.algorithms.scheduling.postprocess import reschedule_stream_plan
optimizer_plan = reschedule_stream_plan(initial_evaluations,
goal_facts,
copy.copy(domain),
(stream_plan + optimizer_results),
unique_binding=True)
if not is_plan(optimizer_plan):
return None
return optimizer_plan + function_plan
def optimistic_process_streams(evaluations,
streams,
double_bindings=None,
unit_efforts=False,
max_effort=INF):
# TODO: iteratively increase max_effort to bias towards easier streams to start
# TODO: cut off instantiation using max_effort
# TODO: make each repeated optimistic object have ordinal more effort
# TODO: enforce that the search uses one optimistic object before claiming the next (like in my first version)
# Can even fall back on converting streams to test streams
# Additive max effort in case something requires a long sequence to achieve
results = []
#effort_from_fact = {}
instantiator = Instantiator(evaluations, streams)
while instantiator.stream_queue:
instance = instantiator.stream_queue.popleft()
if not is_double_bound(instance, double_bindings):
continue
effort = get_instance_effort(instance, unit_efforts)
#op = sum # max | sum
#total_effort = effort + op(effort_from_fact[fact] for fact in instance.get_domain())
if max_effort <= effort:
continue
for stream_result in instance.next_optimistic():
for fact in stream_result.get_certified():
#effort_from_fact[fact] = min(effort_from_fact.get(fact, INF), effort)
instantiator.add_atom(evaluation_from_fact(fact))
results.append(
stream_result) # TODO: don't readd if all repeated facts?
return results
def retrace_instantiation(fact, streams, evaluations, free_parameters,
visited_facts, planned_results):
# Makes two assumptions:
# 1) Each stream achieves a "primary" fact that uses all of its inputs + outputs
# 2) Outputs are only free parameters (no constants)
if (evaluation_from_fact(fact) in evaluations) or (fact in visited_facts):
return
visited_facts.add(fact)
for stream in streams:
for cert in stream.certified:
if get_prefix(fact) == get_prefix(cert):
mapping = get_mapping(get_args(cert),
get_args(fact)) # Should be same anyways
if not all(p in mapping
for p in (stream.inputs + stream.outputs)):
# TODO: assumes another effect is sufficient for binding
# Create arbitrary objects for inputs/outputs that aren't mentioned
# Can lead to incorrect ordering
continue
input_objects = tuple(mapping[p] for p in stream.inputs)
output_objects = tuple(mapping[p] for p in stream.outputs)
if not all(out in free_parameters for out in output_objects):
# Can only bind if free
continue
instance = stream.get_instance(input_objects)
for new_fact in instance.get_domain():
retrace_instantiation(new_fact, streams, evaluations,
free_parameters, visited_facts,
planned_results)
planned_results.append(instance.get_result(output_objects))
def stream_plan_complexity(evaluations, stream_plan, stream_calls, complexity_op=COMPLEXITY_OP):
if not is_plan(stream_plan):
return INF
# TODO: difference between a result having a particular complexity and the next result having something
#optimistic_facts = {}
optimistic_facts = {fact: evaluations[evaluation_from_fact(fact)].complexity
for fact in stream_plan_preimage(stream_plan)}
result_complexities = []
#complexity = 0
for i, result in enumerate(stream_plan):
# if result.external.get_complexity(num_calls=INF) == 0: # TODO: skip if true
result_complexity = complexity_op([0] + [optimistic_facts[fact]
#optimistic_complexity(evaluations, optimistic_facts, fact)
for fact in result.get_domain()])
# if stream_calls is None:
# num_calls = result.instance.num_calls
# else:
num_calls = stream_calls[i]
result_complexity += result.external.get_complexity(num_calls)
result_complexities.append(result_complexity)
#complexity = complexity_op(complexity, result_complexity)
for fact in result.get_certified():
if fact not in optimistic_facts:
optimistic_facts[fact] = result_complexity
complexity = complexity_op([0] + result_complexities)
return complexity
def stream_plan_complexity(evaluations, stream_plan, stream_calls=None):
if not is_plan(stream_plan):
return INF
# TODO: difference between a result having a particular complexity and the next result having something
optimistic_facts = {}
total_complexity = 0
for i, result in enumerate(stream_plan):
result_complexity = 0
for fact in result.get_domain():
evaluation = evaluation_from_fact(fact)
if evaluation in evaluations:
fact_complexity = evaluations[evaluation].complexity
else:
fact_complexity = optimistic_facts[fact]
result_complexity = COMPLEXITY_OP(result_complexity,
fact_complexity)
if stream_calls is None:
result_complexity += result.instance.num_calls + 1
elif i < len(stream_calls):
result_complexity += stream_calls[i] + 1
else:
result_complexity += 1
for fact in result.get_certified():
if fact not in optimistic_facts:
optimistic_facts[fact] = result_complexity
total_complexity = COMPLEXITY_OP(total_complexity, result_complexity)
return total_complexity
def retrace_instantiation(fact, streams, evaluations, visited_facts,
planned_results):
if (evaluation_from_fact(fact) in evaluations) or (fact in visited_facts):
return
visited_facts.add(fact)
for stream in streams:
for cert in stream.certified:
if get_prefix(fact) == get_prefix(cert):
mapping = get_mapping(get_args(cert),
get_args(fact)) # Should be same anyways
if not all(p in mapping
for p in (stream.inputs + stream.outputs)):
# TODO: assumes another effect is sufficient for binding
# Create arbitrary objects for inputs/outputs that aren't mentioned
# Can lead to incorrect ordering
continue
input_objects = tuple(mapping[p] for p in stream.inputs)
output_objects = tuple(mapping[p] for p in stream.outputs)
if not all(
isinstance(out, OptimisticObject)
for out in output_objects):
# Can only bind if free
continue
instance = stream.get_instance(input_objects)
for new_fact in instance.get_domain():
retrace_instantiation(new_fact, streams, evaluations,
visited_facts, planned_results)
result = instance.get_result(output_objects)
planned_results.append(result)
def add_certified(evaluations, result):
new_evaluations = []
for fact in result.get_certified():
evaluation = evaluation_from_fact(fact)
if evaluation not in evaluations:
evaluations[evaluation] = result
new_evaluations.append(evaluation)
return new_evaluations
def static_opt_gen_fn(*input_values):
instance = stream.get_instance(objects_from_values(input_values))
if all(
evaluation_from_fact(f) in evaluations
for f in instance.get_domain()):
return
for output_values in stream.opt_gen_fn(*input_values):
yield output_values
def static_fn(*input_values):
instance = stream.get_instance(objects_from_values(input_values))
if all(
evaluation_from_fact(f) in evaluations
for f in instance.get_domain()):
return None
return tuple(
FutureValue(stream.name, input_values, o) for o in stream.outputs)
def optimistic_process_instance(instantiator, instance):
for result in instance.next_optimistic():
new_facts = False
complexity = instantiator.compute_complexity(instance)
for fact in result.get_certified():
new_facts |= instantiator.add_atom(evaluation_from_fact(fact), complexity)
if isinstance(result, FunctionResult) or new_facts:
yield result
def add_facts(evaluations, fact, result=None):
new_evaluations = []
for fact in fact:
evaluation = evaluation_from_fact(fact)
if evaluation not in evaluations:
evaluations[evaluation] = result
new_evaluations.append(evaluation)
return new_evaluations
def enable(self, evaluations, domain):
if not self.disabled:
return
#if self._disabled_axiom is not None:
# self.external.disabled_instances.remove(self)
# domain.axioms.remove(self._disabled_axiom)
# self._disabled_axiom = None
#super(StreamInstance, self).enable(evaluations, domain) # TODO: strange infinite loop bug if enabled?
evaluations.pop(evaluation_from_fact(self.get_blocked_fact()), None)
def replan_with_optimizers(evaluations, external_plan, domain, externals):
# TODO: return multiple plans?
# TODO: can instead have multiple goal binding combinations
# TODO: can replan using samplers as well
if not is_plan(external_plan):
return external_plan
optimizer_streams = list(
filter(lambda s: type(s) in [VariableStream, ConstraintStream],
externals))
if not optimizer_streams:
return external_plan
stream_plan, function_plan = partition_external_plan(external_plan)
goal_facts = set()
for result in stream_plan:
goal_facts.update(
filter(lambda f: evaluation_from_fact(f) not in evaluations,
result.get_certified()))
visited_facts = set()
new_results = []
for fact in goal_facts:
retrace_instantiation(fact, optimizer_streams, evaluations,
visited_facts, new_results)
variable_results = filter(lambda r: isinstance(r.external, VariableStream),
new_results)
constraint_results = filter(
lambda r: isinstance(r.external, ConstraintStream), new_results)
new_results = variable_results + constraint_results # TODO: ensure correct ordering
#from pddlstream.algorithms.scheduling.recover_streams import get_achieving_streams, extract_stream_plan
#node_from_atom = get_achieving_streams(evaluations, stream_results) # TODO: make these lower effort
#extract_stream_plan(node_from_atom, target_facts, stream_plan)
optimizer_results = []
for optimizer in {get_optimizer(r)
for r in new_results
}: # None is like a unique optimizer:
relevant_results = [
r for r in new_results if get_optimizer(r) == optimizer
]
optimizer_results.extend(
combine_optimizer_plan(relevant_results, function_plan))
#print(str_from_object(set(map(fact_from_evaluation, evaluations))))
#print(str_from_object(set(goal_facts)))
# TODO: can do the flexibly sized optimizers search
from pddlstream.algorithms.scheduling.postprocess import reschedule_stream_plan
combined_plan = reschedule_stream_plan(evaluations,
goal_facts,
copy.copy(domain),
(stream_plan + optimizer_results),
unique_binding=True,
unit_efforts=True)
if not is_plan(combined_plan):
return external_plan
return combined_plan + function_plan
def optimistic_process_instance(instantiator, instance, verbose=False):
for result in instance.next_optimistic():
if verbose:
print(result) # TODO: make a debug tools that reports the optimistic streams
new_facts = False
complexity = instantiator.compute_complexity(instance)
for fact in result.get_certified():
new_facts |= instantiator.add_atom(evaluation_from_fact(fact), complexity)
if isinstance(result, FunctionResult) or new_facts:
yield result
def accelerate_best_bindings(self):
# TODO: reset the values for old streams
for skeleton in self.skeletons:
for _, result in sorted(skeleton.best_binding.bound_results.items(), key=itemgetter(0)):
# TODO: just accelerate the facts within the plan preimage
result.call_index = 0 # Pretends the fact was first
new_complexity = result.compute_complexity(self.evaluations)
for fact in result.get_certified():
evaluation = evaluation_from_fact(fact)
if new_complexity < self.evaluations[evaluation].complexity:
self.evaluations[evaluation] = EvaluationNode(new_complexity, result)
def evaluations_from_stream_plan(evaluations, stream_results, max_effort=INF):
opt_evaluations = set(evaluations)
for result in stream_results:
assert(not result.instance.disabled)
assert(not result.instance.enumerated)
domain = set(map(evaluation_from_fact, result.instance.get_domain()))
assert(domain <= opt_evaluations)
opt_evaluations.update(map(evaluation_from_fact, result.get_certified()))
node_from_atom = get_achieving_streams(evaluations, stream_results)
result_from_evaluation = {evaluation_from_fact(f): n.result
for f, n in node_from_atom.items() if n.effort < max_effort}
return result_from_evaluation
def accelerate_best_bindings(self):
# TODO: reset the values for old streams
for skeleton in self.skeletons:
for _, result in sorted(
skeleton.best_binding.bound_results.items(),
key=itemgetter(0)):
# TODO: just accelerate the facts within the plan preimage
for fact in result.get_certified():
evaluation = evaluation_from_fact(fact)
if evaluation in self.evaluations: # In the event the fact is returned twice
del self.evaluations[evaluation]
result.call_index = 0 # Pretends the fact was first
add_certified(self.evaluations, result)
def optimistic_process_streams(evaluations, streams, double_bindings=None):
instantiator = Instantiator(evaluations, streams)
stream_results = []
while instantiator.stream_queue:
stream_instance = instantiator.stream_queue.popleft()
if not is_double_bound(stream_instance, double_bindings):
continue
for stream_result in stream_instance.next_optimistic():
for fact in stream_result.get_certified():
instantiator.add_atom(evaluation_from_fact(fact))
stream_results.append(
stream_result) # TODO: don't readd if all repeated facts?
return stream_results
def create_visualizations(evaluations, stream_plan, iteration):
# TODO: place it in the temp_dir?
# TODO: decompose any joint streams
for result in stream_plan:
if isinstance(result, SynthStreamResult):
create_synthesizer_visualizations(result, iteration)
filename = ITERATION_TEMPLATE.format(POST_PROCESS if iteration is None else iteration)
# visualize_stream_plan(stream_plan, path)
constraints = set() # TODO: approximates needed facts using produced ones
for stream in stream_plan:
constraints.update(filter(lambda f: evaluation_from_fact(f) not in evaluations, stream.get_certified()))
visualize_constraints(constraints, os.path.join(CONSTRAINT_NETWORK_DIR, filename))
visualize_stream_plan_bipartite(decompose_stream_plan(stream_plan), os.path.join(STREAM_PLAN_DIR, filename))
visualize_stream_plan_bipartite(stream_plan, os.path.join(STREAM_PLAN_DIR, 'fused_' + filename))
def process_immediate_stream_plan(evaluations, stream_plan, disabled, verbose):
new_evaluations = []
for opt_result in stream_plan:
instance = opt_result.instance
if set(map(evaluation_from_fact,
instance.get_domain())) <= evaluations:
disable_stream_instance(instance, disabled)
for result in instance.next_results(verbose=verbose):
for fact in result.get_certified():
evaluation = evaluation_from_fact(fact)
#evaluations.add(evaluation) # To be used on next iteration
new_evaluations.append(evaluation)
evaluations.update(new_evaluations)
return new_evaluations
def evaluate_functions(evaluations, stream_results):
stream_plan = []
for opt_result in stream_results:
instance = opt_result.instance
# TODO: this seems like a bug (it also previously was ... <= evaluations)
if isinstance(instance.external, Function) and \
all(evaluation_from_fact(f) in evaluations for f in instance.get_domain()):
new_results, new_facts = instance.next_results()
assert not new_facts
for result in new_results:
add_certified(evaluations, result)
else:
stream_plan.append(opt_result)
return stream_plan
def plan_streams(evaluations, goal_expression, domain, all_results, negative, effort_weight, max_effort,
simultaneous=False, reachieve=True, replan_actions=set(), **kwargs):
# TODO: alternatively could translate with stream actions on real opt_state and just discard them
# TODO: only consider axioms that have stream conditions?
#reachieve = reachieve and not using_optimizers(all_results)
#for i, result in enumerate(all_results):
# print(i, result, result.get_effort())
applied_results, deferred_results = partition_results(
evaluations, all_results, apply_now=lambda r: not (simultaneous or r.external.info.simultaneous))
stream_domain, deferred_from_name = add_stream_actions(domain, deferred_results)
if reachieve and not using_optimizers(all_results):
achieved_results = {n.result for n in evaluations.values() if isinstance(n.result, Result)}
init_evaluations = {e for e, n in evaluations.items() if n.result not in achieved_results}
applied_results = achieved_results | set(applied_results)
evaluations = init_evaluations # For clarity
# TODO: could iteratively increase max_effort
node_from_atom = get_achieving_streams(evaluations, applied_results, # TODO: apply to all_results?
max_effort=max_effort)
opt_evaluations = {evaluation_from_fact(f): n.result for f, n in node_from_atom.items()}
if UNIVERSAL_TO_CONDITIONAL or using_optimizers(all_results):
goal_expression = add_unsatisfiable_to_goal(stream_domain, goal_expression)
temporal = isinstance(stream_domain, SimplifiedDomain)
optimistic_fn = solve_optimistic_temporal if temporal else solve_optimistic_sequential
instantiated, action_instances, temporal_plan, cost = optimistic_fn(
domain, stream_domain, applied_results, all_results, opt_evaluations,
node_from_atom, goal_expression, effort_weight, **kwargs)
if action_instances is None:
return FAILED, FAILED, cost
action_instances, axiom_plans = recover_axioms_plans(instantiated, action_instances)
# TODO: extract out the minimum set of conditional effects that are actually required
#simplify_conditional_effects(instantiated.task, action_instances)
stream_plan, action_instances = recover_simultaneous(
applied_results, negative, deferred_from_name, action_instances)
action_plan = transform_plan_args(map(pddl_from_instance, action_instances), obj_from_pddl)
replan_step = min([step+1 for step, action in enumerate(action_plan)
if action.name in replan_actions] or [len(action_plan)+1]) # step after action application
stream_plan, opt_plan = recover_stream_plan(evaluations, stream_plan, opt_evaluations, goal_expression, stream_domain,
node_from_atom, action_instances, axiom_plans, negative, replan_step)
if temporal_plan is not None:
# TODO: handle deferred streams
assert all(isinstance(action, Action) for action in opt_plan.action_plan)
opt_plan.action_plan[:] = temporal_plan
return stream_plan, opt_plan, cost
def disable(self, evaluations, domain):
#assert not self.disabled
super(StreamInstance, self).disable(evaluations, domain)
if not self.external.is_fluent(): # self.fluent_facts:
if self.external.is_negated() and not self.successes:
evaluations[evaluation_from_fact(self.get_blocked_fact())] = INTERNAL
return
if self.axiom_predicate is not None:
return
index = len(self.external.disabled_instances)
self.external.disabled_instances.append(self)
self.axiom_predicate = '_ax{}-{}'.format(self.external.blocked_predicate, index)
evaluations[evaluation_from_fact(self.get_blocked_fact())] = INTERNAL
# TODO: allow reporting back which components lead to failure
import pddl
static_fact = (self.axiom_predicate,) + self.external.inputs
preconditions = [static_fact] + list(self.fluent_facts)
self.disabled_axiom = pddl.Axiom(name=self.external.blocked_predicate,
parameters=make_parameters(self.external.inputs),
num_external_parameters=len(self.external.inputs),
condition=make_preconditions(preconditions))
domain.axioms.append(self.disabled_axiom)
def sequence_results(evaluations, combined_results):
current_facts = set()
for result in combined_results:
current_facts.update(filter(lambda f: evaluation_from_fact(f) in evaluations, result.get_domain()))
combined_plan = []
while combined_results:
for result in combined_results:
if set(result.get_domain()) <= current_facts:
combined_plan.append(result)
current_facts.update(result.get_certified())
combined_results.remove(result)
break
else: # TODO: can also just try one cluster and return
return None
return combined_plan
def evaluations_from_stream_plan(evaluations, stream_plan):
result_from_evaluation = {e: None for e in evaluations}
opt_evaluations = set(evaluations)
for result in stream_plan:
if isinstance(result, StreamResult):
effort = result.instance.get_effort()
if effort == INF:
continue
assert (not result.instance.disabled)
assert (not result.instance.enumerated)
domain = set(map(evaluation_from_fact, result.instance.get_domain()))
if not (domain <= opt_evaluations):
continue
for fact in result.get_certified():
evaluation = evaluation_from_fact(fact)
if evaluation not in result_from_evaluation:
result_from_evaluation[evaluation] = result
opt_evaluations.add(evaluation)
return result_from_evaluation
def simultaneous_stream_plan(evaluations,
goal_expression,
domain,
stream_results,
negated,
unit_costs=True,
**kwargs):
if negated:
raise NotImplementedError()
function_evaluations = {e: None for e in evaluations}
for result in stream_results:
if isinstance(result, FunctionResult):
for fact in result.get_certified():
function_evaluations[evaluation_from_fact(fact)] = result
new_domain, stream_result_from_name = add_stream_actions(
domain, stream_results)
combined_plan, _ = solve_finite(function_evaluations,
goal_expression,
new_domain,
unit_costs=unit_costs,
**kwargs)
if combined_plan is None:
return None, None, INF # TODO: return plan cost
stream_plan = []
action_plan = []
for name, args in combined_plan:
if name in stream_result_from_name:
stream_plan.append(stream_result_from_name[name])
else:
action_plan.append((name, args))
action_cost = len(action_plan)
function_plan = set()
if not unit_costs:
action_cost = 0
results_from_head = get_results_from_head(function_evaluations)
for name, args in action_plan:
action = find(lambda a: a.name == name, domain.actions)
pddl_args = tuple(map(pddl_from_object, args))
function_plan.update(
extract_function_results(results_from_head, action, pddl_args))
action_cost += get_cost(domain, results_from_head, name, args)
return (stream_plan + list(function_plan)), action_plan, action_cost
def parse_problem(problem, stream_info={}):
# TODO: just return the problem if already written programmatically
domain_pddl, constant_map, stream_pddl, stream_map, init, goal = problem
domain = parse_domain(domain_pddl)
if len(domain.types) != 1:
raise NotImplementedError('Types are not currently supported')
obj_from_constant = parse_constants(domain, constant_map)
streams = parse_stream_pddl(stream_pddl, stream_map, stream_info)
evaluations = OrderedDict(
(evaluation_from_fact(obj_from_value_expression(f)),
INITIAL_EVALUATION) for f in init)
goal_expression = obj_from_value_expression(goal)
check_problem(domain, streams, obj_from_constant)
parse_goal(goal_expression, domain) # Just to check that it parses
#normalize_domain_goal(domain, goal_expression)
# TODO: refactor the following?
compile_to_exogenous(evaluations, domain, streams)
compile_fluent_streams(domain, streams)
enforce_simultaneous(domain, streams)
return evaluations, goal_expression, domain, streams
