def plan_subgoals(sas_task, subgoal_plan, temp_dir, **kwargs):
full_plan = []
full_cost = 0
for subgoal in subgoal_plan:
sas_task.goal.pairs = subgoal
write_sas_task(sas_task, temp_dir)
plan, cost = run_search(temp_dir, debug=True, **kwargs)
if plan is None:
return None, INF
full_plan.extend(plan)
full_cost += cost
for sas_action in parse_sas_plan(sas_task, plan):
apply_sas_operator(sas_task.init, sas_action)
return full_plan, full_cost
def abstrips_solve_from_task(sas_task,
temp_dir=TEMP_DIR,
clean=False,
debug=False,
hierarchy=[],
**kwargs):
# Like partial order planning in terms of precondition order
# TODO: add achieve subgoal actions
# TODO: most generic would be a heuristic on each state
if hierarchy is None:
return solve_from_task(sas_task,
temp_dir=temp_dir,
clean=clean,
debug=debug,
**kwargs)
if hierarchy == SERIALIZE:
return serialized_solve_from_task(sas_task,
temp_dir=temp_dir,
clean=clean,
debug=debug,
**kwargs)
start_time = time()
plan, cost = None, INF
with Verbose(debug):
print('\n' + 50 * '-' + '\n')
last_plan = []
for level in range(len(hierarchy) + 1):
local_sas_task = deepcopy(sas_task)
prune_hierarchy_pre_eff(
local_sas_task, hierarchy[level:]) # TODO: break if no pruned
add_subgoals(local_sas_task, last_plan)
write_sas_task(local_sas_task, temp_dir)
plan, cost = run_search(temp_dir, debug=True, **kwargs)
if (level == len(hierarchy)) or (plan is None):
# TODO: fall back on standard search
break
last_plan = [
name_from_action(action, args) for action, args in plan
]
if clean:
safe_rm_dir(temp_dir)
print('Total runtime:', time() - start_time)
return plan, cost
def solve_from_task(sas_task,
temp_dir=TEMP_DIR,
clean=False,
debug=False,
hierarchy=[],
**search_args):
# TODO: can solve using another planner and then still translate using FastDownward
# Can apply plan constraints (skeleton constraints) here as well
start_time = time()
with Verbose(debug):
print('\n' + 50 * '-' + '\n')
write_sas_task(sas_task, temp_dir)
solution = run_search(temp_dir, debug=True, **search_args)
if clean:
safe_rm_dir(temp_dir)
print('Total runtime:', time() - start_time)
#for axiom in sas_task.axioms:
# # TODO: return the set of axioms here as well
# var, value = axiom.effect
# print(sas_task.variables.value_names[var])
# axiom.dump()
return solution