def add_optimizer_effects(instantiated, node_from_atom):
# TODO: instantiate axioms with negative on effects for blocking
# TODO: fluent streams using conditional effects. Special fluent predicate for inputs to constraint
# TODO: bug! The FD instantiator prunes the result.external.stream_fact
for instance in instantiated.actions:
# TODO: need to handle case where a negative preconditions is used in an optimizer
for condition, effect in (instance.add_effects + instance.del_effects):
for literal in condition:
fact = fact_from_fd(literal)
if (fact in node_from_atom) and (node_from_atom[fact].result
is not None):
raise NotImplementedError(literal)
facts = get_instance_facts(instance, node_from_atom)
stream_plan = []
extract_stream_plan(node_from_atom, facts, stream_plan)
# TODO: can detect if some of these are simultaneous and add them as preconditions
for result in stream_plan:
#if isinstance(result.external, ComponentStream):
if True: # TODO: integrate sampler and optimizer treatments
# TODO: need to make multiple versions if several ways of achieving the action
atom = fd_from_fact(result.stream_fact)
instantiated.atoms.add(atom)
effect = (tuple(), atom)
instance.add_effects.append(effect)
instance.effect_mappings.append(effect + (None, None))
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 add_stream_costs(node_from_atom, instantiated, unit_efforts, effort_weight):
# TODO: instantiate axioms with negative on effects for blocking
# TODO: fluent streams using conditional effects. Special fluent predicate for inputs to constraint
# This strategy will only work for relaxed to ensure that the current state is applied
for instance in instantiated.actions:
# TODO: prune stream actions here?
# Ignores conditional effect costs
facts = []
for precondition in get_literals(instance.action.precondition):
if precondition.negated:
continue
args = [instance.var_mapping.get(arg, arg) for arg in precondition.args]
literal = precondition.__class__(precondition.predicate, args)
fact = fact_from_fd(literal)
if fact in node_from_atom:
facts.append(fact)
#effort = COMBINE_OP([0] + [node_from_atom[fact].effort for fact in facts])
stream_plan = []
extract_stream_plan(node_from_atom, facts, stream_plan)
if unit_efforts:
effort = len(stream_plan)
else:
effort = scale_cost(sum([0] + [r.instance.get_effort() for r in stream_plan]))
if effort_weight is not None:
instance.cost += effort_weight*effort
# TODO: bug! The FD instantiator prunes the result.external.stream_fact
for result in stream_plan:
# TODO: need to make multiple versions if several ways of achieving the action
if is_optimizer_result(result):
fact = substitute_expression(result.external.stream_fact, result.get_mapping())
atom = fd_from_fact(fact)
instantiated.atoms.add(atom)
effect = (tuple(), atom)
instance.add_effects.append(effect)
def recover_stream_plan(evaluations, current_plan, opt_evaluations,
goal_expression, domain, node_from_atom, action_plan,
axiom_plans, negative):
# Universally quantified conditions are converted into negative axioms
# Existentially quantified conditions are made additional preconditions
# Universally quantified effects are instantiated by doing the cartesian produce of types (slow)
# Added effects cancel out removed effects
# TODO: node_from_atom is a subset of opt_evaluations (only missing functions)
real_task = task_from_domain_problem(
domain, get_problem(evaluations, goal_expression, domain))
opt_task = task_from_domain_problem(
domain, get_problem(opt_evaluations, goal_expression, domain))
negative_from_name = get_negative_predicates(negative)
real_states, combined_plan = recover_negative_axioms(
real_task, opt_task, axiom_plans, action_plan, negative_from_name)
function_plan = compute_function_plan(opt_evaluations, action_plan)
full_preimage = plan_preimage(combined_plan, [])
stream_preimage = set(full_preimage) - real_states[0]
negative_preimage = set(
filter(lambda a: a.predicate in negative_from_name, stream_preimage))
function_plan.update(
convert_negative(negative_preimage, negative_from_name, full_preimage,
real_states))
positive_preimage = stream_preimage - negative_preimage
step_from_fact = {
fact_from_fd(l): full_preimage[l]
for l in positive_preimage if not l.negated
}
target_facts = {
fact
for fact in step_from_fact.keys() if get_prefix(fact) != EQ
}
#stream_plan = reschedule_stream_plan(evaluations, target_facts, domain, stream_results)
# visualize_constraints(map(fact_from_fd, target_facts))
stream_plan = []
for result in current_plan:
if isinstance(result.external, Function) or (result.external
in negative):
function_plan.add(
result) # Prevents these results from being pruned
else:
stream_plan.append(result)
curr_evaluations = evaluations_from_stream_plan(evaluations,
stream_plan,
max_effort=None)
extraction_facts = target_facts - set(
map(fact_from_evaluation, curr_evaluations))
extract_stream_plan(node_from_atom, extraction_facts, stream_plan)
stream_plan = postprocess_stream_plan(evaluations, domain, stream_plan,
target_facts)
stream_plan = convert_fluent_streams(stream_plan, real_states, action_plan,
step_from_fact, node_from_atom)
return stream_plan + list(function_plan)
def add_stream_efforts(node_from_atom, instantiated, effort_weight, **kwargs):
if effort_weight is None:
return
# TODO: make effort just a multiplier (or relative) to avoid worrying about the scale
# TODO: regularize & normalize across the problem?
#efforts = []
for instance in instantiated.actions:
# TODO: prune stream actions here?
# TODO: round each effort individually to penalize multiple streams
facts = get_instance_facts(instance, node_from_atom)
#effort = COMBINE_OP([0] + [node_from_atom[fact].effort for fact in facts])
stream_plan = []
extract_stream_plan(node_from_atom, facts, stream_plan)
effort = compute_plan_effort(stream_plan, **kwargs)
instance.cost += scale_cost(effort_weight * effort)
def recover_stream_plan(evaluations, current_plan, opt_evaluations,
goal_expression, domain, node_from_atom, action_plan,
axiom_plans, negative, replan_step):
# Universally quantified conditions are converted into negative axioms
# Existentially quantified conditions are made additional preconditions
# Universally quantified effects are instantiated by doing the cartesian produce of types (slow)
# Added effects cancel out removed effects
# TODO: node_from_atom is a subset of opt_evaluations (only missing functions)
real_task = task_from_domain_problem(
domain, get_problem(evaluations, goal_expression, domain))
opt_task = task_from_domain_problem(
domain, get_problem(opt_evaluations, goal_expression, domain))
negative_from_name = {
external.blocked_predicate: external
for external in negative if external.is_negated()
}
real_states, full_plan = recover_negative_axioms(real_task, opt_task,
axiom_plans, action_plan,
negative_from_name)
function_plan = compute_function_plan(opt_evaluations, action_plan)
full_preimage = plan_preimage(full_plan,
[]) # Does not contain the stream preimage!
negative_preimage = set(
filter(lambda a: a.predicate in negative_from_name, full_preimage))
negative_plan = convert_negative(negative_preimage, negative_from_name,
full_preimage, real_states)
function_plan.update(negative_plan)
# TODO: OrderedDict for these plans
# TODO: this assumes that actions do not negate preimage goals
positive_preimage = {
l
for l in (set(full_preimage) - real_states[0] - negative_preimage)
if not l.negated
}
steps_from_fact = {
fact_from_fd(l): full_preimage[l]
for l in positive_preimage
}
last_from_fact = {
fact: min(steps)
for fact, steps in steps_from_fact.items() if get_prefix(fact) != EQ
}
#stream_plan = reschedule_stream_plan(evaluations, target_facts, domain, stream_results)
# visualize_constraints(map(fact_from_fd, target_facts))
for result, step in function_plan.items():
for fact in result.get_domain():
last_from_fact[fact] = min(step, last_from_fact.get(fact, INF))
# TODO: get_steps_from_stream
stream_plan = []
last_from_stream = dict(function_plan)
for result in current_plan: # + negative_plan?
# TODO: actually compute when these are needed + dependencies
last_from_stream[result] = 0
if isinstance(result.external, Function) or (result.external
in negative):
if len(action_plan) != replan_step:
raise NotImplementedError(
) # TODO: deferring negated optimizers
# Prevents these results from being pruned
function_plan[result] = replan_step
else:
stream_plan.append(result)
curr_evaluations = evaluations_from_stream_plan(evaluations,
stream_plan,
max_effort=None)
extraction_facts = set(last_from_fact) - set(
map(fact_from_evaluation, curr_evaluations))
extract_stream_plan(node_from_atom, extraction_facts, stream_plan)
# Recomputing due to postprocess_stream_plan
stream_plan = postprocess_stream_plan(evaluations, domain, stream_plan,
last_from_fact)
node_from_atom = get_achieving_streams(evaluations,
stream_plan,
max_effort=None)
fact_sequence = [set(result.get_domain())
for result in stream_plan] + [extraction_facts]
for facts in reversed(fact_sequence): # Bellman ford
for fact in facts: # could flatten instead
result = node_from_atom[fact].result
if result is None:
continue
step = last_from_fact[fact] if result.is_deferrable() else 0
last_from_stream[result] = min(step,
last_from_stream.get(result, INF))
for domain_fact in result.instance.get_domain():
last_from_fact[domain_fact] = min(
last_from_stream[result],
last_from_fact.get(domain_fact, INF))
stream_plan.extend(function_plan)
# Useful to recover the correct DAG
partial_orders = set()
for child in stream_plan:
# TODO: account for fluent objects
for fact in child.get_domain():
parent = node_from_atom[fact].result
if parent is not None:
partial_orders.add((parent, child))
#stream_plan = topological_sort(stream_plan, partial_orders)
bound_objects = set()
for result in stream_plan:
if (last_from_stream[result]
== 0) or not result.is_deferrable(bound_objects=bound_objects):
for ancestor in get_ancestors(result, partial_orders) | {result}:
# TODO: this might change descendants of ancestor. Perform in a while loop.
last_from_stream[ancestor] = 0
if isinstance(ancestor, StreamResult):
bound_objects.update(out for out in ancestor.output_objects
if out.is_unique())
#local_plan = [] # TODO: not sure what this was for
#for fact, step in sorted(last_from_fact.items(), key=lambda pair: pair[1]): # Earliest to latest
# print(step, fact)
# extract_stream_plan(node_from_atom, [fact], local_plan, last_from_fact, last_from_stream)
# Each stream has an earliest evaluation time
# When computing the latest, use 0 if something isn't deferred
# Evaluate each stream as soon as possible
# Option to defer streams after a point in time?
# TODO: action costs for streams that encode uncertainty
state = set(real_task.init)
remaining_results = list(stream_plan)
first_from_stream = {}
#assert 1 <= replan_step # Plan could be empty
for step, instance in enumerate(action_plan):
for result in list(remaining_results):
# TODO: could do this more efficiently if need be
domain = result.get_domain() + get_fluent_domain(result)
if conditions_hold(state, map(fd_from_fact, domain)):
remaining_results.remove(result)
certified = {
fact
for fact in result.get_certified()
if get_prefix(fact) != EQ
}
state.update(map(fd_from_fact, certified))
if step != 0:
first_from_stream[result] = step
# TODO: assumes no fluent axiom domain conditions
apply_action(state, instance)
#assert not remaining_results # Not true if retrace
if first_from_stream:
replan_step = min(replan_step, *first_from_stream.values())
eager_plan = []
results_from_step = defaultdict(list)
for result in stream_plan:
earliest_step = first_from_stream.get(result, 0)
latest_step = last_from_stream.get(result, 0)
assert earliest_step <= latest_step
defer = replan_step <= latest_step
if not defer:
eager_plan.append(result)
# We only perform a deferred evaluation if it has all deferred dependencies
# TODO: make a flag that also allows dependencies to be deferred
future = (earliest_step != 0) or defer
if future:
future_step = latest_step if defer else earliest_step
results_from_step[future_step].append(result)
# TODO: some sort of obj side-effect bug that requires obj_from_pddl to be applied last (likely due to fluent streams)
eager_plan = convert_fluent_streams(eager_plan, real_states, action_plan,
steps_from_fact, node_from_atom)
combined_plan = []
for step, action in enumerate(action_plan):
combined_plan.extend(result.get_action()
for result in results_from_step[step])
combined_plan.append(
transform_action_args(pddl_from_instance(action), obj_from_pddl))
# TODO: the returned facts have the same side-effect bug as above
# TODO: annotate when each preimage fact is used
preimage_facts = {
fact_from_fd(l)
for l in full_preimage if (l.predicate != EQ) and not l.negated
}
for negative_result in negative_plan: # TODO: function_plan
preimage_facts.update(negative_result.get_certified())
for result in eager_plan:
preimage_facts.update(result.get_domain())
# Might not be able to regenerate facts involving the outputs of streams
preimage_facts.update(
result.get_certified()) # Some facts might not be in the preimage
# TODO: record streams and axioms
return eager_plan, OptPlan(combined_plan, preimage_facts)
def recover_stream_plan(evaluations, current_plan, opt_evaluations,
goal_expression, domain, node_from_atom, action_plan,
axiom_plans, negative, replan_step):
# Universally quantified conditions are converted into negative axioms
# Existentially quantified conditions are made additional preconditions
# Universally quantified effects are instantiated by doing the cartesian produce of types (slow)
# Added effects cancel out removed effects
# TODO: node_from_atom is a subset of opt_evaluations (only missing functions)
real_task = task_from_domain_problem(
domain, get_problem(evaluations, goal_expression, domain))
opt_task = task_from_domain_problem(
domain, get_problem(opt_evaluations, goal_expression, domain))
negative_from_name = {
external.blocked_predicate: external
for external in negative if external.is_negated()
}
real_states, combined_plan = recover_negative_axioms(
real_task, opt_task, axiom_plans, action_plan, negative_from_name)
function_plan = compute_function_plan(opt_evaluations, action_plan)
# TODO: record the supporting facts
full_preimage = plan_preimage(combined_plan, [])
stream_preimage = set(full_preimage) - real_states[0]
negative_preimage = set(
filter(lambda a: a.predicate in negative_from_name, stream_preimage))
function_plan.update(
convert_negative(negative_preimage, negative_from_name, full_preimage,
real_states))
positive_preimage = stream_preimage - negative_preimage
steps_from_fact = {
fact_from_fd(l): full_preimage[l]
for l in positive_preimage if not l.negated
}
target_facts = {
fact
for fact in steps_from_fact.keys() if get_prefix(fact) != EQ
}
#stream_plan = reschedule_stream_plan(evaluations, target_facts, domain, stream_results)
# visualize_constraints(map(fact_from_fd, target_facts))
# TODO: get_steps_from_stream
stream_plan = []
step_from_stream = {}
for result in current_plan:
# TODO: actually compute when these are needed + dependencies
step_from_stream[result] = 0
if isinstance(result.external, Function) or (result.external
in negative):
function_plan.add(
result) # Prevents these results from being pruned
else:
stream_plan.append(result)
curr_evaluations = evaluations_from_stream_plan(evaluations,
stream_plan,
max_effort=None)
extraction_facts = target_facts - set(
map(fact_from_evaluation, curr_evaluations))
step_from_fact = {
fact: min(steps_from_fact[fact])
for fact in extraction_facts
}
extract_stream_plan(node_from_atom, extraction_facts, stream_plan,
step_from_fact, step_from_stream)
stream_plan = postprocess_stream_plan(evaluations, domain, stream_plan,
target_facts)
eager_plan = []
actions_from_step = {}
for result in (stream_plan + list(function_plan)):
if (result.opt_index != 0) or (step_from_stream.get(result, 0) <
replan_step):
eager_plan.append(result)
else:
actions_from_step.setdefault(step_from_stream[result],
[]).append(result.get_action())
eager_plan = convert_fluent_streams(eager_plan, real_states, action_plan,
steps_from_fact, node_from_atom)
# print(action_plan)
# # TODO: propagate this forward in the future
# start_from_stream = {}
# for result in eager_plan:
# stuff = list(map(fd_from_fact, get_fluent_domain(result)))
# index = len(real_states)
# for i, state in enumerate(real_states):
# if conditions_hold(state, stuff):
# start_from_stream[result] = i
# index = i
# break
# #else:
# #start_from_stream[result] = len(real_states)
# print(index, result)
# TODO: some sort of obj side-effect bug that requires obj_from_pddl to be applied last (likely due to fluent streams)
#action_plan = transform_plan_args(map(pddl_from_instance, action_instances), obj_from_pddl)
for step, action in enumerate(action_plan):
actions_from_step.setdefault(step, []).append(
transform_action_args(pddl_from_instance(action), obj_from_pddl))
action_plan = list(
flatten(actions_from_step[step] for step in sorted(actions_from_step)))
return eager_plan, action_plan
def recover_stream_plan(evaluations, goal_expression, domain, stream_results, action_plan, negative, unit_costs):
import pddl
import instantiate
# Universally quantified conditions are converted into negative axioms
# Existentially quantified conditions are made additional preconditions
# Universally quantified effects are instantiated by doing the cartesian produce of types (slow)
# Added effects cancel out removed effects
real_task = task_from_domain_problem(domain, get_problem(evaluations, goal_expression, domain, unit_costs))
node_from_atom = get_achieving_streams(evaluations, stream_results)
opt_evaluations = apply_streams(evaluations, stream_results)
opt_task = task_from_domain_problem(domain, get_problem(opt_evaluations, goal_expression, domain, unit_costs))
function_assignments = {fact.fluent: fact.expression for fact in opt_task.init # init_facts
if isinstance(fact, pddl.f_expression.FunctionAssignment)}
type_to_objects = instantiate.get_objects_by_type(opt_task.objects, opt_task.types)
results_from_head = get_results_from_head(opt_evaluations)
action_instances = instantiate_actions(opt_task, type_to_objects, function_assignments, action_plan)
negative_from_name = get_negative_predicates(negative)
axioms_from_name = get_derived_predicates(opt_task.axioms)
opt_task.init = set(opt_task.init)
real_states = [set(real_task.init)] # TODO: had old way of doing this (~July 2018)
preimage_plan = []
function_plan = set()
for layer in action_instances:
for pair, action_instance in layer:
axiom_plan = extract_axiom_plan(opt_task, action_instance, negative_from_name,
static_state=real_states[-1])
if axiom_plan is None:
continue
simplify_conditional_effects(real_states[-1], opt_task.init, action_instance, axioms_from_name)
preimage_plan.extend(axiom_plan + [action_instance])
apply_action(opt_task.init, action_instance)
real_states.append(set(real_states[-1]))
apply_action(real_states[-1], action_instance)
if not unit_costs and (pair is not None):
function_result = extract_function_results(results_from_head, *pair)
if function_result is not None:
function_plan.add(function_result)
break
else:
raise RuntimeError('No action instances are applicable')
# TODO: could instead just accumulate difference between real and opt
full_preimage = plan_preimage(preimage_plan, [])
stream_preimage = set(full_preimage) - real_states[0]
negative_preimage = set(filter(lambda a: a.predicate in negative_from_name, stream_preimage))
positive_preimage = stream_preimage - negative_preimage
function_plan.update(convert_negative(negative_preimage, negative_from_name, full_preimage, real_states))
step_from_fact = {fact_from_fd(l): full_preimage[l] for l in positive_preimage if not l.negated}
target_facts = list(step_from_fact.keys())
#stream_plan = reschedule_stream_plan(evaluations, target_facts, domain, stream_results)
stream_plan = []
extract_stream_plan(node_from_atom, target_facts, stream_plan)
stream_plan = prune_stream_plan(evaluations, stream_plan, target_facts)
stream_plan = convert_fluent_streams(stream_plan, real_states, step_from_fact, node_from_atom)
# visualize_constraints(map(fact_from_fd, stream_preimage))
if DO_RESCHEDULE: # TODO: detect this based on unique or not
# TODO: maybe test if partial order between two ways of achieving facts, if not prune
new_stream_plan = reschedule_stream_plan(evaluations, target_facts, domain, stream_plan)
if new_stream_plan is not None:
stream_plan = new_stream_plan
return stream_plan + list(function_plan)
