def pddl_from_expression(expression):
if isinstance(expression, Object) or isinstance(expression,
OptimisticObject):
return pddl_from_object(expression)
if isinstance(expression, str):
return expression
return '({})'.format(' '.join(map(pddl_from_expression, expression)))
def get_problem(evaluations, goal_exp, domain, unit_costs=False):
objects = objects_from_evaluations(evaluations)
typed_objects = list(
{make_object(pddl_from_object(obj))
for obj in objects} - set(domain.constants))
# TODO: this doesn't include =
init = [
fd_from_evaluation(e) for e in evaluations if not is_negated_atom(e)
]
goal = pddl.Truth() if goal_exp is None else parse_goal(goal_exp, domain)
problem_pddl = None
if USE_FORBID:
problem_pddl = get_problem_pddl(evaluations,
goal_exp,
domain.pddl,
temporal=False)
write_pddl(domain.pddl, problem_pddl)
return Problem(task_name=domain.name,
task_domain_name=domain.name,
objects=sorted(typed_objects, key=lambda o: o.name),
task_requirements=pddl.tasks.Requirements([]),
init=init,
goal=goal,
use_metric=not unit_costs,
pddl=problem_pddl)
def get_problem(init_evaluations, goal_expression, domain, unit_costs=False):
objects = objects_from_evaluations(init_evaluations)
typed_objects = list({pddl.TypedObject(pddl_from_object(obj), OBJECT) for obj in objects} - set(domain.constants))
# TODO: this doesn't include =
init = [fd_from_evaluation(e) for e in init_evaluations if not is_negated_atom(e)]
goal = parse_goal(goal_expression, domain)
return Problem(task_name=domain.name, task_domain_name=domain.name, objects=sorted(typed_objects, key=lambda o: o.name),
task_requirements=pddl.tasks.Requirements([]), init=init, goal=goal, use_metric=not unit_costs)
def enforce_single_binding(result, preconditions, effects):
binding_facts = [(BOUND_PREDICATE, pddl_from_object(out))
for out in result.output_objects]
preconditions.extend(Not(fact) for fact in binding_facts)
effects.extend(fact for fact in binding_facts)