def simultaneous_stream_plan(evaluations,
goal_expression,
domain,
stream_results,
negated,
unit_costs=False,
**kwargs):
if negated:
raise NotImplementedError(negated)
for result in stream_results:
if isinstance(result.external, Stream) and result.external.is_fluent():
raise NotImplementedError('Fluents are not supported')
applied_streams, deferred_streams = partition_results(
evaluations, stream_results, lambda r: False)
opt_evaluations = apply_streams(evaluations, applied_streams)
stream_domain, stream_result_from_name = add_stream_actions(
domain, deferred_streams)
if any(map(is_optimizer_result, stream_results)):
goal_expression = augment_goal(stream_domain, goal_expression)
combined_plan, _ = solve_finite(opt_evaluations,
goal_expression,
stream_domain,
unit_costs=unit_costs,
**kwargs)
if combined_plan is None:
return None, INF
stream_plan, action_plan = partition_plan(combined_plan,
stream_result_from_name)
function_plan = extract_function_plan(opt_evaluations, action_plan, domain,
unit_costs)
cost = get_plan_cost(opt_evaluations, action_plan, domain, unit_costs)
combined_plan = stream_plan + function_plan + action_plan
return combined_plan, cost
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 simultaneous_stream_plan(evaluations,
goal_expression,
domain,
stream_results,
negated,
unit_costs=False,
**kwargs):
# TODO: remove this method in favor of the more general relaxed plan
if negated:
raise NotImplementedError(negated)
for result in stream_results:
if isinstance(result.external, Stream) and result.external.is_fluent():
raise NotImplementedError('Fluents are not supported')
# TODO: warning check if using simultaneous_stream_plan with non-eager functions
applied_streams, deferred_streams = partition_results(
evaluations, stream_results, lambda r: False)
opt_evaluations = apply_streams(evaluations, applied_streams)
stream_domain, stream_result_from_name = add_stream_actions(
domain, deferred_streams)
if any(map(is_optimizer_result, stream_results)):
goal_expression = add_unsatisfiable_to_goal(stream_domain,
goal_expression)
combined_plan, _ = solve_finite(opt_evaluations,
goal_expression,
stream_domain,
unit_costs=unit_costs,
**kwargs)
if combined_plan is None:
return None, INF
stream_plan, action_plan = partition_plan(combined_plan,
stream_result_from_name)
function_plan = extract_function_plan(opt_evaluations, action_plan, domain,
unit_costs)
cost = get_plan_cost(opt_evaluations, action_plan, domain, unit_costs)
combined_plan = stream_plan + function_plan + action_plan
return combined_plan, cost
def relaxed_stream_plan(evaluations, goal_expression, domain, stream_results, negative, unit_costs,
unit_efforts, effort_weight, 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, stream_results,
apply_now=lambda r: not r.external.info.simultaneous)
stream_domain, result_from_name = add_stream_actions(domain, deferred_results)
node_from_atom = get_achieving_streams(evaluations, applied_results)
opt_evaluations = apply_streams(evaluations, applied_results) # if n.effort < INF
if any(map(is_optimizer_result, stream_results)):
goal_expression = augment_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
if (effort_weight is not None) or any(map(is_optimizer_result, applied_results)):
add_stream_costs(node_from_atom, instantiated, unit_efforts, effort_weight)
add_optimizer_axioms(stream_results, instantiated)
with Verbose(debug):
sas_task = sas_from_instantiated(instantiated)
sas_task.metric = True
#sas_task = sas_from_pddl(task)
#action_plan, _ = serialized_solve_from_task(sas_task, debug=debug, **kwargs)
action_plan, _ = abstrips_solve_from_task(sas_task, debug=debug, **kwargs)
#action_plan, _ = abstrips_solve_from_task_sequential(sas_task, debug=debug, **kwargs)
if action_plan is None:
return None, INF
applied_plan, function_plan = partition_external_plan(recover_stream_plan(
evaluations, goal_expression, stream_domain, applied_results, action_plan, negative, unit_costs))
deferred_plan, action_plan = partition_plan(action_plan, result_from_name)
stream_plan = applied_plan + deferred_plan + function_plan
action_plan = obj_from_pddl_plan(action_plan)
cost = get_plan_cost(opt_evaluations, action_plan, domain, unit_costs)
combined_plan = stream_plan + action_plan
return combined_plan, cost
def plan_streams(evaluations, goal_expression, domain, all_results, negative, effort_weight, max_effort,
simultaneous=False, reachieve=True, **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 UNIVERSAL_TO_CONDITIONAL or using_optimizers(all_results):
goal_expression = add_unsatisfiable_to_goal(stream_domain, goal_expression)
instantiated, action_instances, action_plan, cost = solve_optimistic_sequential(
domain, stream_domain, applied_results, all_results, opt_evaluations, node_from_atom, goal_expression, effort_weight, **kwargs)
if action_plan is None:
return action_plan, cost
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)
stream_plan = recover_stream_plan(evaluations, stream_plan, opt_evaluations, goal_expression,
stream_domain, node_from_atom, action_instances, axiom_plans, negative)
combined_plan = stream_plan + action_plan
return combined_plan, cost
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 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