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_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_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
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:
task = task_from_domain_problem(
domain, get_problem(evaluations, goal_exp, domain, unit_costs))
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
def solve_temporal(evaluations, goal_exp, domain, debug=False, **kwargs):
assert isinstance(domain, SimplifiedDomain)
problem = get_problem_pddl(evaluations, goal_exp, domain.pddl)
return solve_tfd(domain.pddl, problem, debug=debug)