def relaxed_stream_plan(evaluations,
goal_expression,
domain,
all_results,
negative,
unit_efforts,
effort_weight,
max_effort,
simultaneous=False,
reachieve=True,
unit_costs=False,
debug=False,
**kwargs):
# TODO: alternatively could translate with stream actions on real opt_state and just discard them
# TODO: only consider axioms that have stream conditions?
applied_results, deferred_results = partition_results(
evaluations,
all_results,
apply_now=lambda r: not (simultaneous or r.external.info.simultaneous))
stream_domain, result_from_name = add_stream_actions(
domain, deferred_results)
opt_evaluations = apply_streams(evaluations,
applied_results) # if n.effort < INF
if reachieve:
achieved_results = {
r
for r in evaluations.values() if isinstance(r, Result)
}
init_evaluations = {
e
for e, r in evaluations.items() if r 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,
unit_efforts=unit_efforts,
max_effort=max_effort)
if using_optimizers(all_results):
goal_expression = add_unsatisfiable_to_goal(stream_domain,
goal_expression)
problem = get_problem(opt_evaluations, goal_expression, stream_domain,
unit_costs) # begin_metric
with Verbose(debug):
instantiated = instantiate_task(
task_from_domain_problem(stream_domain, problem))
if instantiated is None:
return None, INF
cost_from_action = {action: action.cost for action in instantiated.actions}
if (effort_weight is not None) or using_optimizers(applied_results):
add_stream_efforts(node_from_atom,
instantiated,
effort_weight,
unit_efforts=unit_efforts)
add_optimizer_axioms(all_results, instantiated)
action_from_name = rename_instantiated_actions(instantiated)
with Verbose(debug):
sas_task = sas_from_instantiated(instantiated)
sas_task.metric = True
# TODO: apply renaming to hierarchy as well
# solve_from_task | serialized_solve_from_task | abstrips_solve_from_task | abstrips_solve_from_task_sequential
action_plan, _ = solve_from_task(sas_task, debug=debug, **kwargs)
if action_plan is None:
return None, INF
action_instances = [action_from_name[name] for name, _ in action_plan]
cost = get_plan_cost(action_instances, cost_from_action, unit_costs)
axiom_plans = recover_axioms_plans(instantiated, action_instances)
applied_plan, function_plan = partition_external_plan(
recover_stream_plan(evaluations, opt_evaluations, goal_expression,
stream_domain, node_from_atom, action_instances,
axiom_plans, negative, unit_costs))
#action_plan = obj_from_pddl_plan(parse_action(instance.name) for instance in action_instances)
action_plan = obj_from_pddl_plan(map(pddl_from_instance, action_instances))
deferred_plan, action_plan = partition_plan(action_plan, result_from_name)
stream_plan = applied_plan + deferred_plan + function_plan
combined_plan = stream_plan + action_plan
return combined_plan, cost
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):
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 = 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, 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)
def sequential_stream_plan(evaluations, goal_expression, domain, stream_results, negated, unit_costs=True, **kwargs):
if negated:
raise NotImplementedError()
# TODO: compute preimage and make that the goal instead
opt_evaluations = evaluations_from_stream_plan(evaluations, stream_results)
opt_task = task_from_domain_problem(domain, get_problem(opt_evaluations, goal_expression, domain, unit_costs))
action_plan, action_cost = solve_from_task(opt_task, **kwargs)
if action_plan is None:
return None, action_cost
import instantiate
fluent_facts = MockSet()
init_facts = set()
task = task_from_domain_problem(domain, get_problem(evaluations, goal_expression, domain, unit_costs))
type_to_objects = instantiate.get_objects_by_type(task.objects, task.types)
task.actions, stream_result_from_name = get_stream_actions(stream_results)
results_from_head = get_results_from_head(opt_evaluations)
# TODO: add ordering constraints to simplify the optimization
import pddl
action_from_name = {}
function_plan = set()
for i, (name, args) in enumerate(action_plan):
action = find_unique(lambda a: a.name == name, domain.actions)
assert(len(action.parameters) == len(args))
#parameters = action.parameters[:action.num_external_parameters]
var_mapping = {p.name: a for p, a in zip(action.parameters, args)}
new_name = '{}-{}'.format(name, i)
new_parameters = action.parameters[len(args):]
new_preconditions = []
action.precondition.instantiate(var_mapping, init_facts, fluent_facts, new_preconditions)
new_effects = []
for eff in action.effects:
eff.instantiate(var_mapping, init_facts, fluent_facts, type_to_objects, new_effects)
new_effects = [pddl.Effect([], pddl.Conjunction(conditions), effect)
for conditions, effect in new_effects]
cost = pddl.Increase(fluent=pddl.PrimitiveNumericExpression(symbol=TOTAL_COST, args=[]),
expression=pddl.NumericConstant(1))
#cost = None
task.actions.append(pddl.Action(new_name, new_parameters, 0,
pddl.Conjunction(new_preconditions), new_effects, cost))
action_from_name[new_name] = (name, map(obj_from_pddl, args))
if not unit_costs:
function_plan.update(extract_function_results(results_from_head, action, args))
planner = kwargs.get('planner', 'ff-astar')
combined_plan, _ = solve_from_task(task, planner=planner, **kwargs)
if combined_plan is None:
return None, obj_from_pddl_plan(action_plan), INF
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(action_from_name[name])
stream_plan += list(function_plan)
combined_plan = stream_plan + action_plan
return combined_plan, action_cost
def plan_streams(evaluations,
goal_expression,
domain,
all_results,
negative,
effort_weight,
max_effort,
simultaneous=False,
reachieve=True,
debug=False,
**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)
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 using_optimizers(all_results):
goal_expression = add_unsatisfiable_to_goal(stream_domain,
goal_expression)
problem = get_problem(opt_evaluations, goal_expression,
stream_domain) # begin_metric
with Verbose(debug):
instantiated = instantiate_task(
task_from_domain_problem(stream_domain, problem))
if instantiated is None:
return None, INF
if using_optimizers(all_results):
# TODO: reachieve=False when using optimizers or should add applied facts
instantiate_optimizer_axioms(instantiated, evaluations,
goal_expression, domain, all_results)
cost_from_action = {action: action.cost for action in instantiated.actions}
add_stream_efforts(node_from_atom, instantiated, effort_weight)
if using_optimizers(applied_results):
add_optimizer_effects(instantiated, node_from_atom)
action_from_name = rename_instantiated_actions(instantiated)
with Verbose(debug):
sas_task = sas_from_instantiated(instantiated)
sas_task.metric = True
# TODO: apply renaming to hierarchy as well
# solve_from_task | serialized_solve_from_task | abstrips_solve_from_task | abstrips_solve_from_task_sequential
action_plan, raw_cost = solve_from_task(sas_task, debug=debug, **kwargs)
#print(raw_cost)
if action_plan is None:
return None, INF
action_instances = [action_from_name[name] for name, _ in action_plan]
simplify_conditional_effects(instantiated.task, action_instances)
stream_plan, action_instances = recover_simultaneous(
applied_results, negative, deferred_from_name, action_instances)
cost = get_plan_cost(action_instances, cost_from_action)
axiom_plans = recover_axioms_plans(instantiated, action_instances)
stream_plan = recover_stream_plan(evaluations, stream_plan,
opt_evaluations, goal_expression,
stream_domain, node_from_atom,
action_instances, axiom_plans, negative)
#action_plan = obj_from_pddl_plan(parse_action(instance.name) for instance in action_instances)
action_plan = obj_from_pddl_plan(map(pddl_from_instance, action_instances))
combined_plan = stream_plan + action_plan
return combined_plan, cost