def solve_optimistic_sequential(domain, stream_domain, applied_results, all_results,
opt_evaluations, node_from_atom, goal_expression,
effort_weight, debug=False, **kwargs):
#print(sorted(map(fact_from_evaluation, opt_evaluations)))
temporal_plan = None
problem = get_problem(opt_evaluations, goal_expression, stream_domain) # begin_metric
with Verbose(verbose=False):
instantiated = instantiate_task(task_from_domain_problem(stream_domain, problem))
if instantiated is None:
return instantiated, None, temporal_plan, INF
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)
# TODO: reachieve=False when using optimizers or should add applied facts
instantiate_optimizer_axioms(instantiated, domain, all_results)
action_from_name = rename_instantiated_actions(instantiated, RENAME_ACTIONS)
# TODO: the action unsatisfiable conditions are pruned
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
renamed_plan, _ = solve_from_task(sas_task, debug=debug, **kwargs)
if renamed_plan is None:
return instantiated, None, temporal_plan, INF
action_instances = [action_from_name[name if RENAME_ACTIONS else '({} {})'.format(name, ' '.join(args))]
for name, args in renamed_plan]
cost = get_plan_cost(action_instances, cost_from_action)
return instantiated, action_instances, temporal_plan, cost
def solve_optimistic_sequential(domain, stream_domain, applied_results, all_results,
opt_evaluations, node_from_atom, goal_expression,
effort_weight, debug=False, **kwargs):
if isinstance(stream_domain, SimplifiedDomain):
return solve_optimistic_temporal(domain, stream_domain, applied_results, all_results,
opt_evaluations, node_from_atom, goal_expression,
effort_weight, debug=debug, **kwargs)
problem = get_problem(opt_evaluations, goal_expression, stream_domain) # begin_metric
with Verbose():
instantiated = instantiate_task(task_from_domain_problem(stream_domain, problem))
if instantiated is None:
return instantiated, None, None, INF
cost_from_action = add_stream_efforts(node_from_atom, instantiated, effort_weight)
if using_optimizers(applied_results):
add_optimizer_effects(instantiated, node_from_atom)
# TODO: reachieve=False when using optimizers or should add applied facts
instantiate_optimizer_axioms(instantiated, domain, all_results)
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
renamed_plan, _ = solve_from_task(sas_task, debug=debug, **kwargs)
if renamed_plan is None:
return instantiated, None, None, INF
action_instances = [action_from_name[name] for name, _ in renamed_plan]
cost = get_plan_cost(action_instances, cost_from_action)
# plan = obj_from_pddl_plan(parse_action(instance.name) for instance in action_instances)
plan = obj_from_pddl_plan(map(pddl_from_instance, action_instances))
return instantiated, action_instances, plan, cost