def retrace_instantiation(fact, streams, evaluations, free_parameters,
visited_facts, planned_results):
# Makes two assumptions:
# 1) Each stream achieves a "primary" fact that uses all of its inputs + outputs
# 2) Outputs are only free parameters (no constants)
if (evaluation_from_fact(fact) in evaluations) or (fact in visited_facts):
return
visited_facts.add(fact)
for stream in streams:
for cert in stream.certified:
if get_prefix(fact) == get_prefix(cert):
mapping = get_mapping(get_args(cert),
get_args(fact)) # Should be same anyways
if not all(p in mapping
for p in (stream.inputs + stream.outputs)):
# TODO: assumes another effect is sufficient for binding
# Create arbitrary objects for inputs/outputs that aren't mentioned
# Can lead to incorrect ordering
continue
input_objects = safe_apply_mapping(stream.inputs, mapping)
output_objects = safe_apply_mapping(stream.outputs, mapping)
if not all(out in free_parameters for out in output_objects):
# Can only bind if free
continue
instance = stream.get_instance(input_objects)
for new_fact in instance.get_domain():
retrace_instantiation(new_fact, streams, evaluations,
free_parameters, visited_facts,
planned_results)
planned_results.append(instance.get_result(output_objects))
def fn(literal, action):
if literal.predicate not in predicate_map:
return literal
# TODO: other checks on only inputs
stream = predicate_map[literal.predicate]
mapping = remap_certified(literal, stream)
if mapping is None:
# TODO: this excludes typing. This is not entirely safe
return literal
output_args = set(mapping[arg] for arg in stream.outputs)
if isinstance(action, pddl.Action): # TODO: unified Action/Axiom effects
for effect in action.effects:
if isinstance(effect, pddl.Effect) and (output_args & set(effect.literal.args)):
raise RuntimeError('Fluent stream outputs cannot be in action effects: {}'.format(
effect.literal.predicate))
elif not stream.is_negated:
axiom = action
raise RuntimeError('Fluent stream outputs cannot be in an axiom: {}'.format(axiom.name))
blocked_args = safe_apply_mapping(stream.inputs, mapping)
blocked_literal = literal.__class__(stream.blocked_predicate, blocked_args).negate()
if stream.is_negated:
conditions = [blocked_literal]
conditions.extend(pddl.Atom(get_prefix(fact), safe_apply_mapping(get_args(fact), mapping)) # fd_from_fact
for fact in stream.domain) # TODO: be careful when using imply
return pddl.Conjunction(conditions) # TODO: prune redundant conditions
return pddl.Conjunction([literal, blocked_literal])
def _add_combinations(self, stream, atoms):
if not all(atoms):
return
domain = list(map(head_from_fact, stream.domain))
# Most constrained variable/atom to least constrained
for combo in product(*atoms):
mapping = test_mapping(domain, combo)
if mapping is not None:
input_objects = safe_apply_mapping(stream.inputs, mapping)
self.push_instance(stream.get_instance(input_objects))
def opt_from_graph(names, orders, infos={}):
param_from_order = {order: PARAM_TEMPLATE.format(*order) for order in orders}
fact_from_order = {order: (PREDICATE, param_from_order[order]) for order in orders}
object_from_param = {param: parse_value(param) for param in param_from_order.values()}
incoming_from_edges, outgoing_from_edges = neighbors_from_orders(orders)
stream_plan = []
for i, n in enumerate(names):
#info = infos.get(n, StreamInfo(p_success=1, overhead=0, verbose=True))
inputs = [param_from_order[n2, n] for n2 in incoming_from_edges[n]]
outputs = [param_from_order[n, n2] for n2 in outgoing_from_edges[n]]
#gen = get_gen(outputs=outputs, p_success=info.p_success)
#gen = get_gen(infos[i], outputs=outputs)
stream = Stream(
name=n,
#gen_fn=DEBUG,
#gen_fn=from_gen(gen),
gen_fn=from_gen_fn(get_gen_fn(outputs=outputs, **infos[i])),
inputs=inputs,
domain=[fact_from_order[n2, n] for n2 in incoming_from_edges[n]],
fluents=[],
outputs=outputs,
certified=[fact_from_order[n, n2] for n2 in outgoing_from_edges[n]],
#info=info,
info=StreamInfo(),
)
# TODO: dump names
print()
print(stream)
input_objects = safe_apply_mapping(stream.inputs, object_from_param)
instance = stream.get_instance(input_objects)
print(instance)
output_objects = safe_apply_mapping(stream.outputs, object_from_param)
result = instance.get_result(output_objects)
print(result)
stream_plan.append(result)
opt_plan = OptPlan(action_plan=[], preimage_facts=[])
opt_solution = OptSolution(stream_plan, opt_plan, cost=1)
return opt_solution
def _add_combinations_relation(self, stream, atoms):
if not all(atoms):
return
# TODO: might be a bug here?
domain = list(map(head_from_fact, stream.domain))
# TODO: compute this first?
relations = [
Relation(filter(is_parameter, domain[index].args), [
tuple(a for a, b in safe_zip(atom.args, domain[index].args)
if is_parameter(b)) for atom in atoms[index]
]) for index in compute_order(domain, atoms)
]
solution = solve_satisfaction(relations)
for element in solution.body:
mapping = solution.get_mapping(element)
input_objects = safe_apply_mapping(stream.inputs, mapping)
self.push_instance(stream.get_instance(input_objects))
def select_inputs(instance, inputs):
external = instance.external
assert set(inputs) <= set(external.inputs)
mapping = get_mapping(external.inputs, instance.input_objects)
return safe_apply_mapping(inputs, mapping)