def examine_instantiated(problem,
constraints=PlanConstraints(),
unit_costs=False,
max_time=INF,
verbose=False,
**search_args):
domain_pddl, constant_map, stream_pddl, _, init, goal = problem
stream_map = DEBUG
problem = PDDLProblem(domain_pddl, constant_map, stream_pddl, stream_map,
init, goal)
evaluations, goal_exp, domain, externals = parse_problem(
problem, constraints=constraints, unit_costs=unit_costs)
store = SolutionStore(evaluations,
max_time,
success_cost=INF,
verbose=verbose)
#externals = compile_fluents_as_attachments(domain, externals) #
instantiator = Instantiator(externals, evaluations)
process_stream_queue(instantiator,
store,
complexity_limit=INF,
verbose=verbose)
#plan, cost = solve_finite(evaluations, goal_exp, domain, max_cost=max_cost, **search_args)
debug = False
assert not isinstance(domain, SimplifiedDomain)
problem = get_problem(evaluations, goal_exp, domain, unit_costs)
task = task_from_domain_problem(domain, problem)
with Verbose(debug):
instantiated = instantiate_task(task)
instantiated = convert_instantiated(instantiated)
return instantiated
def instantiate_optimizer_axioms(instantiated, domain, results):
# Needed for instantiating axioms before adding stream action effects
# Otherwise, FastDownward will prune these unreachable axioms
# TODO: compute this first and then apply the eager actions
stream_init = {
fd_from_fact(result.stream_fact)
for result in results if isinstance(result, StreamResult)
}
evaluations = list(
map(evaluation_from_fd, stream_init | instantiated.atoms))
temp_domain = make_domain(
predicates=[make_predicate(UNSATISFIABLE, [])],
axioms=[ax for ax in domain.axioms if ax.name == UNSATISFIABLE])
temp_problem = get_problem(evaluations, Not((UNSATISFIABLE, )),
temp_domain)
# TODO: UNSATISFIABLE might be in atoms making the goal always infeasible
with Verbose():
# TODO: the FastDownward instantiation prunes static preconditions
use_fd = False if using_optimizers(results) else FD_INSTANTIATE
new_instantiated = instantiate_task(task_from_domain_problem(
temp_domain, temp_problem),
use_fd=use_fd,
check_infeasible=False,
prune_static=False)
assert new_instantiated is not None
instantiated.axioms.extend(new_instantiated.axioms)
instantiated.atoms.update(new_instantiated.atoms)
def solve_optimistic_temporal(domain, stream_domain, applied_results, all_results,
opt_evaluations, node_from_atom, goal_expression,
effort_weight, debug=False, **kwargs):
# TODO: assert that the unused parameters are off
assert domain is stream_domain
#assert len(applied_results) == len(all_results)
problem = get_problem(opt_evaluations, goal_expression, domain)
with Verbose():
instantiated = instantiate_task(task_from_domain_problem(domain, problem))
if instantiated is None:
return instantiated, None, None, INF
problem = get_problem_pddl(opt_evaluations, goal_expression, domain.pddl)
pddl_plan, makespan = solve_tfd(domain.pddl, problem, debug=debug, **kwargs)
if pddl_plan is None:
return instantiated, None, pddl_plan, makespan
instance_from_action_args = defaultdict(list)
for instance in instantiated.actions:
name, args = parse_action(instance)
instance_from_action_args[name, args].append(instance)
#instance.action, instance.var_mapping
action_instances = []
for action in pddl_plan:
instances = instance_from_action_args[action.name, action.args]
if len(instances) != 1:
for action in instances:
action.dump()
#assert len(instances) == 1 # TODO: support 2 <= case
action_instances.append(instances[0])
temporal_plan = obj_from_pddl_plan(pddl_plan) # pddl_plan is sequential
return instantiated, action_instances, temporal_plan, makespan
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 examine_instantiated(problem, constraints=PlanConstraints(), unit_costs=False, unique=False, verbose=False, debug=False):
# TODO: refactor to an analysis file
domain_pddl, constant_map, stream_pddl, _, init, goal = problem
stream_map = DEBUG if unique else SHARED_DEBUG # DEBUG_MODES
problem = PDDLProblem(domain_pddl, constant_map, stream_pddl, stream_map, init, goal)
evaluations, goal_exp, domain, externals = parse_problem(
problem, constraints=constraints, unit_costs=unit_costs)
assert not isinstance(domain, SimplifiedDomain)
# store = SolutionStore(evaluations, max_time, success_cost=INF, verbose=verbose)
# instantiator = Instantiator(externals, evaluations)
# process_stream_queue(instantiator, store, complexity_limit=INF, verbose=verbose)
# results = [] # TODO: extract from process_stream_queue
#set_unique(externals)
# domain.actions[:] = [] # TODO: only instantiate axioms
# TODO: drop all fluents and instantiate
# TODO: relaxed planning version of this
results, exhausted = optimistic_process_streams(evaluations, externals, complexity_limit=INF, max_effort=None)
evaluations = evaluations_from_stream_plan(evaluations, results, max_effort=None)
problem = get_problem(evaluations, goal_exp, domain, unit_costs)
task = task_from_domain_problem(domain, problem)
with Verbose(debug):
instantiated = instantiate_task(task)
if instantiated is None:
return None
# TODO: reinstantiate actions?
instantiated.axioms[:] = [reinstantiate_axiom(axiom) for axiom in instantiated.axioms]
instantiated = convert_instantiated(instantiated)
return results, instantiated
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
def solve_optimistic_temporal(domain, stream_domain, applied_results, all_results,
opt_evaluations, node_from_atom, goal_expression,
effort_weight, debug=False, **kwargs):
assert domain is stream_domain
#assert len(applied_results) == len(all_results)
problem = get_problem(opt_evaluations, goal_expression, domain)
with Verbose():
instantiated = instantiate_task(task_from_domain_problem(domain, problem))
if instantiated is None:
return instantiated, None, None, INF
problem = get_problem_pddl(opt_evaluations, goal_expression, domain.pddl)
pddl_plan, makespan = solve_tfd(domain.pddl, problem, debug=debug)
if pddl_plan is None:
return instantiated, None, pddl_plan, makespan
instance_from_action_args = defaultdict(list)
for instance in instantiated.actions:
tokens = instance.name.strip('()').split(' ')
name, args = tokens[0], tuple(tokens[1:])
instance_from_action_args[name, args].append(instance)
#instance.action, instance.var_mapping
action_instances = []
for action in pddl_plan:
instances = instance_from_action_args[action.name, action.args]
assert len(instances) == 1 # TODO: support 2 <= case
action_instances.append(instances[0])
plan = obj_from_pddl_plan(pddl_plan)
return instantiated, action_instances, plan, makespan
def solve_sequential(evaluations,
goal_exp,
domain,
unit_costs=False,
debug=False,
**search_args):
problem = get_problem(evaluations, goal_exp, domain, unit_costs)
task = task_from_domain_problem(domain, problem)
if has_attachments(domain):
with Verbose(debug):
instantiated = instantiate_task(task)
return solve_pyplanners(instantiated, **search_args)
sas_task = sas_from_pddl(task, debug=debug)
return abstrips_solve_from_task(sas_task, debug=debug, **search_args)
def solve_finite(evaluations, goal_exp, domain, unit_costs=False, debug=False, **search_args):
if isinstance(domain, SimplifiedDomain):
problem = get_problem_pddl(evaluations, goal_exp, domain.pddl)
pddl_plan, cost = solve_tfd(domain.pddl, problem, debug=debug)
else:
problem = get_problem(evaluations, goal_exp, domain, unit_costs)
task = task_from_domain_problem(domain, problem)
if has_attachments(domain):
with Verbose(debug):
instantiated = instantiate_task(task)
pddl_plan, cost = solve_pyplanners(instantiated, **search_args)
else:
sas_task = sas_from_pddl(task, debug=debug)
pddl_plan, cost = abstrips_solve_from_task(sas_task, debug=debug, **search_args)
plan = obj_from_pddl_plan(pddl_plan)
return plan, cost
