def translate_task(strips_to_sas, ranges, translation_key,
mutex_dict, mutex_ranges, mutex_key,
init, goals,
actions, axioms, metric, implied_facts):
with timers.timing("Processing axioms", block=True):
axioms, axiom_init, axiom_layer_dict = axiom_rules.handle_axioms(
actions, axioms, goals)
init = init + axiom_init
#axioms.sort(key=lambda axiom: axiom.name)
#for axiom in axioms:
# axiom.dump()
if options.dump_task:
# Remove init facts that don't occur in strips_to_sas: they're constant.
nonconstant_init = filter(strips_to_sas.get, init)
dump_task(nonconstant_init, goals, actions, axioms, axiom_layer_dict)
init_values = [rang - 1 for rang in ranges]
# Closed World Assumption: Initialize to "range - 1" == Nothing.
for fact in init:
pairs = strips_to_sas.get(fact, []) # empty for static init facts
for var, val in pairs:
curr_val = init_values[var]
if curr_val != ranges[var] - 1 and curr_val != val:
assert False, "Inconsistent init facts! [fact = %s]" % fact
init_values[var] = val
init = sas_tasks.SASInit(init_values)
goal_dict_list = translate_strips_conditions(goals, strips_to_sas, ranges,
mutex_dict, mutex_ranges)
if goal_dict_list is None:
# "None" is a signal that the goal is unreachable because it
# violates a mutex.
return unsolvable_sas_task("Goal violates a mutex")
assert len(goal_dict_list) == 1, "Negative goal not supported"
## we could substitute the negative goal literal in
## normalize.substitute_complicated_goal, using an axiom. We currently
## don't do this, because we don't run into this assertion, if the
## negative goal is part of finite domain variable with only two
## values, which is most of the time the case, and hence refrain from
## introducing axioms (that are not supported by all heuristics)
goal_pairs = list(goal_dict_list[0].items())
goal = sas_tasks.SASGoal(goal_pairs)
operators = translate_strips_operators(actions, strips_to_sas, ranges,
mutex_dict, mutex_ranges,
implied_facts)
axioms = translate_strips_axioms(axioms, strips_to_sas, ranges, mutex_dict,
mutex_ranges)
axiom_layers = [-1] * len(ranges)
for atom, layer in axiom_layer_dict.items():
assert layer >= 0
[(var, val)] = strips_to_sas[atom]
axiom_layers[var] = layer
variables = sas_tasks.SASVariables(ranges, axiom_layers, translation_key)
mutexes = [sas_tasks.SASMutexGroup(group) for group in mutex_key]
return sas_tasks.SASTask(variables, mutexes, init, goal,
operators, axioms, metric)
def translate_task(strips_to_sas, ranges, translation_key, mutex_key,
init, goals, actions, axioms, metric):
axioms, axiom_init, axiom_layer_dict = axiom_rules.handle_axioms(
actions, axioms, goals)
init = init + axiom_init
#axioms.sort(key=lambda axiom: axiom.name)
#for axiom in axioms:
# axiom.dump()
init_values = [rang - 1 for rang in ranges]
# Closed World Assumption: Initialize to "range - 1" == Nothing.
for fact in init:
pairs = strips_to_sas.get(fact, []) # empty for static init facts
for var, val in pairs:
assert init_values[var] == ranges[var] - 1, "Inconsistent init facts!"
init_values[var] = val
init = sas_tasks.SASInit(init_values)
goal_pairs = list(translate_strips_conditions(goals, strips_to_sas, ranges).items())
goal = sas_tasks.SASGoal(goal_pairs)
operators = translate_strips_operators(actions, strips_to_sas, ranges)
axioms = translate_strips_axioms(axioms, strips_to_sas, ranges)
axiom_layers = [-1] * len(ranges)
for atom, layer in axiom_layer_dict.items():
assert layer >= 0
[(var, val)] = strips_to_sas[atom]
axiom_layers[var] = layer
variables = sas_tasks.SASVariables(ranges, axiom_layers, translation_key)
mutexes = [sas_tasks.SASMutexGroup(group) for group in mutex_key]
return sas_tasks.SASTask(variables, mutexes, init, goal,
operators, axioms, metric)
def translate_task(strips_to_sas, ranges, mutex_dict, mutex_ranges, init, goals,
actions, axioms, metric, implied_facts):
with timers.timing("Processing axioms", block=True):
axioms, axiom_init, axiom_layer_dict = axiom_rules.handle_axioms(
actions, axioms, goals)
init = init + axiom_init
#axioms.sort(key=lambda axiom: axiom.name)
#for axiom in axioms:
# axiom.dump()
init_values = [rang - 1 for rang in ranges]
# Closed World Assumption: Initialize to "range - 1" == Nothing.
for fact in init:
pair = strips_to_sas.get(fact)
pairs = strips_to_sas.get(fact, []) # empty for static init facts
for var, val in pairs:
assert init_values[var] == ranges[var] - 1, "Inconsistent init facts!"
init_values[var] = val
init = sas_tasks.SASInit(init_values)
goal_dict_list = translate_strips_goal(goals, strips_to_sas, ranges, mutex_dict, mutex_ranges)
assert len(goal_dict_list) == 1, "Negative goal not supported"
## we could substitute the negative goal literal in
## normalize.substitute_complicated_goal, using an axiom. We currently
## don't do this, because we don't run into this assertion, if the
## negative goal is part of finite domain variable with only two
## values, which is most of the time the case, and hence refrain from
## introducing axioms (that are not supported by all heuristics)
goal_pairs = goal_dict_list[0].items()
goal = sas_tasks.SASGoal(goal_pairs)
operators = translate_strips_operators(actions, strips_to_sas, ranges, mutex_dict, mutex_ranges, implied_facts)
axioms = translate_strips_axioms(axioms, strips_to_sas, ranges, mutex_dict, mutex_ranges)
axiom_layers = [-1] * len(ranges)
for atom, layer in axiom_layer_dict.iteritems():
assert layer >= 0
[(var, val)] = strips_to_sas[atom]
axiom_layers[var] = layer
variables = sas_tasks.SASVariables(ranges, axiom_layers)
return sas_tasks.SASTask(variables, init, goal, operators, axioms, metric)
def translate_task(strips_to_sas, ranges, translation_key,
mutex_dict, mutex_ranges, mutex_key,
init, init_unknown, init_oneof, init_formula, goals,
actions, observation_actions, axioms, metric, implied_facts):
with timers.timing("Processing axioms", block=True):
axioms, axiom_init, axiom_layer_dict = axiom_rules.handle_axioms(
actions, axioms, goals)
init = init + axiom_init
#axioms.sort(key=lambda axiom: axiom.name)
#for axiom in axioms:
# axiom.dump()
if DUMP_TASK:
# Remove init facts that don't occur in strips_to_sas: they're constant.
nonconstant_init = filter(strips_to_sas.get, init)
dump_task(nonconstant_init, goals, actions, axioms, axiom_layer_dict)
# Closed World Assumption
false_facts = list(range(len(ranges)))
for fact in init_unknown:
pairs = strips_to_sas.get(fact, [])
for pair in pairs:
false_facts.remove(pair[0])
facts = []
for fact in init:
assert fact not in init_unknown
pairs = strips_to_sas.get(fact, [])
for pair in pairs:
false_facts.remove(pair[0])
if pairs:
facts = facts + pairs
for var in false_facts:
assert fact not in init_unknown
facts.append((var, ranges[var] - 1))
facts_oneof = []
for oneof in init_oneof:
assert len(oneof) >= 2
for fact in oneof:
assert fact in init_unknown
l = []
for one in oneof:
l = l + strips_to_sas.get(one, [])
facts_oneof.append(l)
# move to conditions.py?
def translate_formula(formula, result, strips_to_sas, ranges, mutex_dict, mutex_ranges):
if isinstance(formula, pddl.conditions.Atom):
assert formula in init_unknown
result.append(strips_to_sas.get(formula, []))
elif isinstance(formula, pddl.conditions.NegatedAtom):
dict_list = translate_strips_conditions([formula], strips_to_sas, ranges, mutex_dict, mutex_ranges)
assert len(dict_list) == 1
result.append(dict_list[0].items())
elif isinstance(formula, pddl.conditions.Disjunction):
result.append("or(")
for part in formula.parts:
translate_formula(part, result, strips_to_sas, ranges, mutex_dict, mutex_ranges)
result.append(")")
elif isinstance(formula, pddl.conditions.Conjunction):
result.append("and(")
for part in formula.parts:
translate_formula(part, result, strips_to_sas, ranges, mutex_dict, mutex_ranges)
result.append(")")
else:
assert False, print(formula)
formulae = []
for formula in init_formula:
result = []
translate_formula(formula, result, strips_to_sas, ranges, mutex_dict, mutex_ranges)
formulae.append(result)
init = sas_tasks.SASInit(facts, facts_oneof, formulae)
goal_dict_list = translate_strips_conditions(goals, strips_to_sas, ranges,
mutex_dict, mutex_ranges)
if goal_dict_list is None:
# "None" is a signal that the goal is unreachable because it
# violates a mutex.
return unsolvable_sas_task("Goal violates a mutex")
assert len(goal_dict_list) == 1, "Negative goal not supported"
## we could substitute the negative goal literal in
## normalize.substitute_complicated_goal, using an axiom. We currently
## don't do this, because we don't run into this assertion, if the
## negative goal is part of finite domain variable with only two
## values, which is most of the time the case, and hence refrain from
## introducing axioms (that are not supported by all heuristics)
goal_pairs = list(goal_dict_list[0].items())
goal = sas_tasks.SASGoal(goal_pairs)
operators = translate_strips_operators(actions, strips_to_sas, ranges,
mutex_dict, mutex_ranges,
implied_facts)
observation_operators = translate_strips_operators(observation_actions,
strips_to_sas, ranges,
mutex_dict, mutex_ranges,
implied_facts)
axioms = translate_strips_axioms(axioms, strips_to_sas, ranges, mutex_dict,
mutex_ranges)
axiom_layers = [-1] * len(ranges)
for atom, layer in axiom_layer_dict.items():
assert layer >= 0
[(var, val)] = strips_to_sas[atom]
axiom_layers[var] = layer
variables = sas_tasks.SASVariables(ranges, axiom_layers, translation_key)
mutexes = [sas_tasks.SASMutexGroup(group) for group in mutex_key]
return sas_tasks.SASTask(variables, mutexes, init, goal,
operators + observation_operators,
axioms, metric)
def translate_task(strips_to_sas, ranges, init, goals, actions,
durative_actions, axioms, num_axioms, num_axioms_by_layer,
max_num_layer, num_axiom_map, const_num_axioms):
axioms, axiom_init, axiom_layer_dict, true_atoms, false_atoms = axiom_rules.handle_axioms(
actions, durative_actions, axioms, goals)
init = init + axiom_init
# filter trivial true_atoms from goal
goals = [g for g in goals if g not in true_atoms
] # FIXME: empty goal would be handled nicely by search
# if any atom in goal is false, the task is unsolvable
for fa in false_atoms:
if fa in goals:
print "False atom in goal:"
fa.dump()
return unsolvable_sas_task("False atom in goal")
comp_axioms = [{}, []]
goal_dict_list = translate_strips_conditions(goals, strips_to_sas, ranges,
comp_axioms)
assert len(goal_dict_list) == 1, "Negative goal not supported"
## we could substitute the negative goal literal in
## normalize.substitute_complicated_goal, using an axiom. We currently
## don't do this, because we don't run into this assertion, if the
## negative goal is part of finite domain variable with only two
## values, which is most of the time the case, and hence refrain from
## introducing axioms (that are not supported by all heuristics)
goal_pairs = goal_dict_list[0].items()
goal = sas_tasks.SASGoal(goal_pairs)
# FIXME: remove this, defunct anyways
operators = translate_strips_operators(actions, strips_to_sas, ranges,
comp_axioms)
temp_operators = translate_temporal_strips_operators(
durative_actions, strips_to_sas, ranges, comp_axioms, true_atoms,
false_atoms)
axioms = translate_strips_axioms(axioms, strips_to_sas, ranges,
comp_axioms)
sas_num_axioms = [
translate_numeric_axiom(axiom, strips_to_sas) for axiom in num_axioms
if axiom not in const_num_axioms and axiom.effect not in num_axiom_map
]
axiom_layers = [-1] * len(ranges)
## each numeric axiom gets its own layer (a wish of a colleague for
## knowledge compilation or search. If you use only the translator,
## you can change this)
num_axiom_layer = 0
for layer in num_axioms_by_layer:
num_axioms_by_layer[layer].sort(lambda x, y: cmp(x.name, y.name))
for axiom in num_axioms_by_layer[layer]:
if axiom.effect not in num_axiom_map:
[(var, val)] = strips_to_sas[axiom.effect]
if layer == -1:
axiom_layers[var] = -1
else:
axiom_layers[var] = num_axiom_layer
num_axiom_layer += 1
for axiom in comp_axioms[1]:
axiom_layers[axiom.effect] = num_axiom_layer
for atom, layer in axiom_layer_dict.iteritems():
assert layer >= 0
[(var, val)] = strips_to_sas[atom]
axiom_layers[var] = layer + num_axiom_layer + 1
variables = sas_tasks.SASVariables(ranges, axiom_layers)
init_values = [rang - 1 for rang in ranges]
# Closed World Assumption: Initialize to "range - 1" == Nothing.
for fact in init:
if isinstance(fact, pddl.Atom):
pairs = strips_to_sas.get(fact, []) # empty for static init facts
for var, val in pairs:
assert init_values[
var] == ranges[var] - 1, "Inconsistent init facts!"
init_values[var] = val
else: # isinstance(fact,pddl.FunctionAssignment)
pairs = strips_to_sas.get(fact.fluent,
[]) #empty for constant functions
for (var, _) in pairs:
val = fact.expression.value
assert init_values[
var] == ranges[var] - 1, "Inconsistent init facts!"
init_values[var] = val
for axiom in const_num_axioms:
var = strips_to_sas.get(axiom.effect)[0][0]
val = axiom.parts[0].value
init_values[var] = val
init = sas_tasks.SASInit(init_values)
return sas_tasks.SASTask(variables, init, goal, operators, temp_operators,
axioms, sas_num_axioms, comp_axioms[1])
def translate_task(strips_to_sas, ranges, translation_key, mutex_dict,
mutex_ranges, mutex_key, init, init_unknown, init_oneof,
init_formula, goals, actions, observation_actions, axioms,
metric, implied_facts):
with timers.timing("Processing axioms", block=True):
axioms, axiom_init, axiom_layer_dict = axiom_rules.handle_axioms(
actions, axioms, goals)
init = init + axiom_init
#axioms.sort(key=lambda axiom: axiom.name)
#for axiom in axioms:
# axiom.dump()
if DUMP_TASK:
# Remove init facts that don't occur in strips_to_sas: they're constant.
nonconstant_init = filter(strips_to_sas.get, init)
dump_task(nonconstant_init, goals, actions, axioms, axiom_layer_dict)
# Closed World Assumption
false_facts = list(range(len(ranges)))
for fact in init_unknown:
pairs = strips_to_sas.get(fact, [])
for pair in pairs:
false_facts.remove(pair[0])
facts = []
for fact in init:
assert fact not in init_unknown
pairs = strips_to_sas.get(fact, [])
for pair in pairs:
false_facts.remove(pair[0])
if pairs:
facts = facts + pairs
for var in false_facts:
for fact in init_unknown:
assert not var == strips_to_sas.get(fact, [])[0][0]
facts.append((var, ranges[var] - 1))
facts_oneof = []
for oneof in init_oneof:
assert len(oneof) >= 2
for fact in oneof:
assert fact in init_unknown
l = []
for one in oneof:
l = l + strips_to_sas.get(one, [])
facts_oneof.append(l)
# move to conditions.py?
def translate_formula(formula, result, strips_to_sas, ranges, mutex_dict,
mutex_ranges):
if isinstance(formula, pddl.conditions.Atom):
assert formula in init_unknown
result.append(strips_to_sas.get(formula, []))
elif isinstance(formula, pddl.conditions.NegatedAtom):
dict_list = translate_strips_conditions([formula], strips_to_sas,
ranges, mutex_dict,
mutex_ranges)
assert len(dict_list) == 1
result.append(dict_list[0].items())
elif isinstance(formula, pddl.conditions.Disjunction):
result.append("or(")
for part in formula.parts:
translate_formula(part, result, strips_to_sas, ranges,
mutex_dict, mutex_ranges)
result.append(")")
elif isinstance(formula, pddl.conditions.Conjunction):
result.append("and(")
for part in formula.parts:
translate_formula(part, result, strips_to_sas, ranges,
mutex_dict, mutex_ranges)
result.append(")")
else:
assert False, print(formula)
formulae = []
for formula in init_formula:
result = []
translate_formula(formula, result, strips_to_sas, ranges, mutex_dict,
mutex_ranges)
formulae.append(result)
init = sas_tasks.SASInit(facts, facts_oneof, formulae)
goal_dict_list = translate_strips_conditions(goals, strips_to_sas, ranges,
mutex_dict, mutex_ranges)
if goal_dict_list is None:
# "None" is a signal that the goal is unreachable because it
# violates a mutex.
return unsolvable_sas_task("Goal violates a mutex")
assert len(goal_dict_list) == 1, "Negative goal not supported"
## we could substitute the negative goal literal in
## normalize.substitute_complicated_goal, using an axiom. We currently
## don't do this, because we don't run into this assertion, if the
## negative goal is part of finite domain variable with only two
## values, which is most of the time the case, and hence refrain from
## introducing axioms (that are not supported by all heuristics)
goal_pairs = list(goal_dict_list[0].items())
goal = sas_tasks.SASGoal(goal_pairs)
operators = translate_strips_operators(actions, strips_to_sas, ranges,
mutex_dict, mutex_ranges,
implied_facts)
observation_operators = translate_strips_operators(observation_actions,
strips_to_sas, ranges,
mutex_dict,
mutex_ranges,
implied_facts)
axioms = translate_strips_axioms(axioms, strips_to_sas, ranges, mutex_dict,
mutex_ranges)
axiom_layers = [-1] * len(ranges)
for atom, layer in axiom_layer_dict.items():
assert layer >= 0
[(var, val)] = strips_to_sas[atom]
axiom_layers[var] = layer
variables = sas_tasks.SASVariables(ranges, axiom_layers, translation_key)
mutexes = [sas_tasks.SASMutexGroup(group) for group in mutex_key]
return sas_tasks.SASTask(variables, mutexes, init, goal,
operators + observation_operators, axioms, metric)
def translate_task(strips_to_sas, ranges, translation_key,
mutex_dict, mutex_ranges, mutex_key,
init, goals,
actions, axioms, metric, implied_facts):
with timers.timing("Processing axioms", block=True):
axioms, axiom_init, axiom_layer_dict = axiom_rules.handle_axioms(
actions, axioms, goals)
init = init + axiom_init
#axioms.sort(key=lambda axiom: axiom.name)
#for axiom in axioms:
# axiom.dump()
if options.dump_task:
# Remove init facts that don't occur in strips_to_sas: they're constant.
nonconstant_init = filter(strips_to_sas.get, init)
dump_task(nonconstant_init, goals, actions, axioms, axiom_layer_dict)
init_values = [rang - 1 for rang in ranges]
# Closed World Assumption: Initialize to "range - 1" == Nothing.
for fact in init:
pairs = strips_to_sas.get(fact, []) # empty for static init facts
for var, val in pairs:
curr_val = init_values[var]
if curr_val != ranges[var] - 1 and curr_val != val:
assert False, "Inconsistent init facts! [fact = %s]" % fact
init_values[var] = val
init = sas_tasks.SASInit(init_values)
goal_dict_list = translate_strips_conditions(goals, strips_to_sas, ranges,
mutex_dict, mutex_ranges)
if goal_dict_list is None:
# "None" is a signal that the goal is unreachable because it
# violates a mutex.
return unsolvable_sas_task("Goal violates a mutex")
assert len(goal_dict_list) == 1, "Negative goal not supported"
## we could substitute the negative goal literal in
## normalize.substitute_complicated_goal, using an axiom. We currently
## don't do this, because we don't run into this assertion, if the
## negative goal is part of finite domain variable with only two
## values, which is most of the time the case, and hence refrain from
## introducing axioms (that are not supported by all heuristics)
goal_pairs = list(goal_dict_list[0].items())
if not goal_pairs:
return solvable_sas_task("Empty goal")
goal = sas_tasks.SASGoal(goal_pairs)
operators = translate_strips_operators(actions, strips_to_sas, ranges,
mutex_dict, mutex_ranges,
implied_facts)
axioms = translate_strips_axioms(axioms, strips_to_sas, ranges, mutex_dict,
mutex_ranges)
axiom_layers = [-1] * len(ranges)
for atom, layer in axiom_layer_dict.items():
assert layer >= 0
[(var, val)] = strips_to_sas[atom]
axiom_layers[var] = layer
variables = sas_tasks.SASVariables(ranges, axiom_layers, translation_key)
mutexes = [sas_tasks.SASMutexGroup(group) for group in mutex_key]
return sas_tasks.SASTask(variables, mutexes, init, goal,
operators, axioms, metric)
def translate_task(strips_to_sas, module_effects_to_sas,
module_groundings_to_sas, ranges, init, goals, actions,
durative_actions, axioms, num_axioms, num_axioms_by_layer,
max_num_layer, num_axiom_map, const_num_axioms, oplinit,
objects, modules, module_inits, module_exits,
subplan_generators, init_constant_predicates,
init_constant_numerics):
axioms, axiom_init, axiom_layer_dict, true_atoms, false_atoms = axiom_rules.handle_axioms(
actions, durative_actions, axioms, goals)
init = init + axiom_init
# filter trivial true_atoms from goal
goals = [g for g in goals if g not in true_atoms
] # FIXME: empty goal would be handled nicely by search
# if any atom in goal is false, the task is unsolvable
for fa in false_atoms:
if fa in goals:
print "False atom in goal:"
fa.dump()
return unsolvable_sas_task("False atom in goal")
comp_axioms = [{}, []]
goal_dict_list = translate_strips_conditions(goals, strips_to_sas, ranges,
comp_axioms)
assert len(goal_dict_list) == 1, "Negative goal not supported"
## we could substitute the negative goal literal in
## normalize.substitute_complicated_goal, using an axiom. We currently
## don't do this, because we don't run into this assertion, if the
## negative goal is part of finite domain variable with only two
## values, which is most of the time the case, and hence refrain from
## introducing axioms (that are not supported by all heuristics)
goal_pairs = goal_dict_list[0].items()
goal = sas_tasks.SASGoal(goal_pairs)
# FIXME: remove this, defunct anyways
operators = translate_strips_operators(actions, strips_to_sas,
module_effects_to_sas, ranges,
comp_axioms)
temp_operators = translate_temporal_strips_operators(
durative_actions, strips_to_sas, module_effects_to_sas,
module_groundings_to_sas, ranges, comp_axioms, true_atoms, false_atoms)
axioms = translate_strips_axioms(axioms, strips_to_sas, ranges,
comp_axioms)
sas_num_axioms = [
translate_numeric_axiom(axiom, strips_to_sas) for axiom in num_axioms
if axiom not in const_num_axioms and axiom.effect not in num_axiom_map
]
axiom_layers = [-1] * len(ranges)
## each numeric axiom gets its own layer (a wish of a colleague for
## knowledge compilation or search. If you use only the translator,
## you can change this)
num_axiom_layer = 0
for layer in num_axioms_by_layer:
num_axioms_by_layer[layer].sort(lambda x, y: cmp(x.name, y.name))
for axiom in num_axioms_by_layer[layer]:
if axiom.effect not in num_axiom_map:
[(var, val)] = strips_to_sas[axiom.effect]
if layer == -1:
axiom_layers[var] = -1
else:
axiom_layers[var] = num_axiom_layer
num_axiom_layer += 1
for axiom in comp_axioms[1]:
axiom_layers[axiom.effect] = num_axiom_layer
for atom, layer in axiom_layer_dict.iteritems():
assert layer >= 0
[(var, val)] = strips_to_sas[atom]
axiom_layers[var] = layer + num_axiom_layer + 1
variables = sas_tasks.SASVariables(ranges, axiom_layers)
init_values = [rang - 1 for rang in ranges]
# Closed World Assumption: Initialize to "range - 1" == Nothing.
for fact in init:
if isinstance(fact, pddl.Atom):
pairs = strips_to_sas.get(fact, []) # empty for static init facts
for var, val in pairs:
assert init_values[
var] == ranges[var] - 1, "Inconsistent init facts!"
init_values[var] = val
else: # isinstance(fact,pddl.FunctionAssignment)
pairs = strips_to_sas.get(fact.fluent,
[]) #empty for constant functions
for (var, _) in pairs:
val = fact.expression.value
assert init_values[
var] == ranges[var] - 1, "Inconsistent init facts!"
init_values[var] = val
for axiom in const_num_axioms:
var = strips_to_sas.get(axiom.effect)[0][0]
val = axiom.parts[0].value
init_values[var] = val
init = sas_tasks.SASInit(init_values)
# TODO: move this block to translate_modules
strips_condition_modules = [
module for module in modules if module.type == "conditionchecker"
]
strips_effect_modules = [
module for module in modules if module.type == "effect"
]
strips_cost_modules = [
module for module in modules if module.type == "cost"
]
strips_grounding_modules = [
module for module in modules if module.type == "grounding"
]
strips_condition_modules.sort(lambda x, y: cmp(str(x), str(y)))
strips_effect_modules.sort(lambda x, y: cmp(str(x), str(y)))
strips_cost_modules.sort(lambda x, y: cmp(str(x), str(y)))
strips_grounding_modules.sort(lambda x, y: cmp(str(x), str(y)))
condition_modules = []
effect_modules = []
cost_modules = []
grounding_modules = []
for mod in strips_condition_modules:
assert mod.parent is not None
sas_params = []
for (ground_param, pddl_param) in zip(mod.parameters,
mod.parent.parameters):
sas_params.append(
(pddl_param.name, ground_param.type, ground_param.name))
# strips_to_sas call is very hacky
assert len(strips_to_sas[mod.toModuleCall()]) == 1
assert len(strips_to_sas[mod.toModuleCall()][0]) == 2
mod_var = strips_to_sas[mod.toModuleCall()][0][0]
condition_modules.append(
sas_tasks.SASConditionModule(mod.modulecall, sas_params, mod_var))
for mod in strips_effect_modules:
assert mod.parent is not None
sas_params = []
for (ground_param, pddl_param) in zip(mod.parameters,
mod.parent.parameters):
sas_params.append(
(pddl_param.name, ground_param.type, ground_param.name))
sas_effs = []
for eff in mod.effects:
assert len(strips_to_sas[eff]) == 1
assert len(strips_to_sas[eff][0]) == 2
sas_effs.append(strips_to_sas[eff][0][0])
# missing eff num + eff vars
effect_modules.append(
sas_tasks.SASEffectModule(
mod.modulecall, sas_params,
module_effects_to_sas[mod.toModuleCall()][0], sas_effs))
for mod in strips_cost_modules:
assert mod.parent is not None # ?
sas_params = []
for (ground_param, pddl_param) in zip(mod.parameters,
mod.parent.parameters):
sas_params.append(
(pddl_param.name, ground_param.type, ground_param.name))
# make sure strips_to_sas is not mixed badly with condition modules
assert len(strips_to_sas[mod.toModuleCall()]) == 1
assert len(strips_to_sas[mod.toModuleCall()][0]) == 2
mod_var = strips_to_sas[mod.toModuleCall()][0][0]
cost_modules.append(
sas_tasks.SASConditionModule(mod.modulecall, sas_params, mod_var))
for mod in strips_grounding_modules:
assert mod.parent is not None
sas_params = []
for (ground_param, pddl_param) in zip(mod.parameters,
mod.parent.parameters):
sas_params.append(
(pddl_param.name, ground_param.type, ground_param.name))
grounding_modules.append(
sas_tasks.SASGroundingModule(
mod.modulecall, sas_params,
module_groundings_to_sas[mod.toModuleCall()][0]))
return sas_tasks.SASTask(variables, init, goal, operators, temp_operators,
axioms, sas_num_axioms, comp_axioms[1], oplinit,
objects, condition_modules, effect_modules,
cost_modules, grounding_modules,
sas_tasks.SASTranslation(strips_to_sas),
module_inits, module_exits, subplan_generators,
init_constant_predicates, init_constant_numerics)
def translate_task(strips_to_sas, module_effects_to_sas, module_groundings_to_sas, ranges, init, goals, actions,
durative_actions, axioms, num_axioms, num_axioms_by_layer,
max_num_layer, num_axiom_map, const_num_axioms, oplinit, objects,
modules, module_inits, module_exits, subplan_generators, init_constant_predicates, init_constant_numerics):
axioms, axiom_init, axiom_layer_dict, true_atoms, false_atoms = axiom_rules.handle_axioms(
actions, durative_actions, axioms, goals)
init = init + axiom_init
# filter trivial true_atoms from goal
goals = [g for g in goals if g not in true_atoms] # FIXME: empty goal would be handled nicely by search
# if any atom in goal is false, the task is unsolvable
for fa in false_atoms:
if fa in goals:
print "False atom in goal:"
fa.dump()
return unsolvable_sas_task("False atom in goal")
comp_axioms = [{},[]]
goal_dict_list = translate_strips_conditions(goals, strips_to_sas, ranges, comp_axioms)
assert len(goal_dict_list) == 1, "Negative goal not supported"
## we could substitute the negative goal literal in
## normalize.substitute_complicated_goal, using an axiom. We currently
## don't do this, because we don't run into this assertion, if the
## negative goal is part of finite domain variable with only two
## values, which is most of the time the case, and hence refrain from
## introducing axioms (that are not supported by all heuristics)
goal_pairs = goal_dict_list[0].items()
goal = sas_tasks.SASGoal(goal_pairs)
# FIXME: remove this, defunct anyways
operators = translate_strips_operators(actions,
strips_to_sas, module_effects_to_sas, ranges, comp_axioms)
temp_operators = translate_temporal_strips_operators(durative_actions,
strips_to_sas, module_effects_to_sas, module_groundings_to_sas, ranges, comp_axioms,
true_atoms, false_atoms)
axioms = translate_strips_axioms(axioms, strips_to_sas, ranges, comp_axioms)
sas_num_axioms = [translate_numeric_axiom(axiom,strips_to_sas) for axiom in num_axioms
if axiom not in const_num_axioms and
axiom.effect not in num_axiom_map]
axiom_layers = [-1] * len(ranges)
## each numeric axiom gets its own layer (a wish of a colleague for
## knowledge compilation or search. If you use only the translator,
## you can change this)
num_axiom_layer = 0
for layer in num_axioms_by_layer:
num_axioms_by_layer[layer].sort(lambda x,y: cmp(x.name,y.name))
for axiom in num_axioms_by_layer[layer]:
if axiom.effect not in num_axiom_map:
[(var,val)] = strips_to_sas[axiom.effect]
if layer == -1:
axiom_layers[var] = -1
else:
axiom_layers[var] = num_axiom_layer
num_axiom_layer += 1
for axiom in comp_axioms[1]:
axiom_layers[axiom.effect] = num_axiom_layer
for atom, layer in axiom_layer_dict.iteritems():
assert layer >= 0
[(var, val)] = strips_to_sas[atom]
axiom_layers[var] = layer + num_axiom_layer + 1
variables = sas_tasks.SASVariables(ranges, axiom_layers)
init_values = [rang - 1 for rang in ranges]
# Closed World Assumption: Initialize to "range - 1" == Nothing.
for fact in init:
if isinstance(fact,pddl.Atom):
pairs = strips_to_sas.get(fact, []) # empty for static init facts
for var, val in pairs:
assert init_values[var] == ranges[var] - 1, "Inconsistent init facts!"
init_values[var] = val
else: # isinstance(fact,pddl.FunctionAssignment)
pairs = strips_to_sas.get(fact.fluent,[]) #empty for constant functions
for (var, _) in pairs:
val = fact.expression.value
assert init_values[var] == ranges[var] - 1, "Inconsistent init facts!"
init_values[var]=val
for axiom in const_num_axioms:
var = strips_to_sas.get(axiom.effect)[0][0]
val = axiom.parts[0].value
init_values[var]=val
init = sas_tasks.SASInit(init_values)
# TODO: move this block to translate_modules
strips_condition_modules = [module for module in modules if module.type == "conditionchecker"]
strips_effect_modules = [module for module in modules if module.type == "effect"]
strips_cost_modules = [module for module in modules if module.type == "cost"]
strips_grounding_modules = [module for module in modules if module.type == "grounding"]
strips_condition_modules.sort(lambda x,y : cmp(str(x), str(y)))
strips_effect_modules.sort(lambda x,y : cmp(str(x), str(y)))
strips_cost_modules.sort(lambda x,y : cmp(str(x), str(y)))
strips_grounding_modules.sort(lambda x,y : cmp(str(x), str(y)))
condition_modules = []
effect_modules = []
cost_modules = []
grounding_modules = []
for mod in strips_condition_modules:
assert mod.parent is not None
sas_params = []
for (ground_param, pddl_param) in zip(mod.parameters, mod.parent.parameters):
sas_params.append((pddl_param.name, ground_param.type, ground_param.name))
# strips_to_sas call is very hacky
assert len(strips_to_sas[mod.toModuleCall()]) == 1
assert len(strips_to_sas[mod.toModuleCall()][0]) == 2
mod_var = strips_to_sas[mod.toModuleCall()][0][0]
condition_modules.append(sas_tasks.SASConditionModule(mod.modulecall, sas_params, mod_var))
for mod in strips_effect_modules:
assert mod.parent is not None
sas_params = []
for (ground_param, pddl_param) in zip(mod.parameters, mod.parent.parameters):
sas_params.append((pddl_param.name, ground_param.type, ground_param.name))
sas_effs = []
for eff in mod.effects:
assert len(strips_to_sas[eff]) == 1
assert len(strips_to_sas[eff][0]) == 2
sas_effs.append(strips_to_sas[eff][0][0])
# missing eff num + eff vars
effect_modules.append(sas_tasks.SASEffectModule(mod.modulecall, sas_params, module_effects_to_sas[mod.toModuleCall()][0], sas_effs))
for mod in strips_cost_modules:
assert mod.parent is not None # ?
sas_params = []
for (ground_param, pddl_param) in zip(mod.parameters, mod.parent.parameters):
sas_params.append((pddl_param.name, ground_param.type, ground_param.name))
# make sure strips_to_sas is not mixed badly with condition modules
assert len(strips_to_sas[mod.toModuleCall()]) == 1
assert len(strips_to_sas[mod.toModuleCall()][0]) == 2
mod_var = strips_to_sas[mod.toModuleCall()][0][0]
cost_modules.append(sas_tasks.SASConditionModule(mod.modulecall, sas_params, mod_var))
for mod in strips_grounding_modules:
assert mod.parent is not None
sas_params = []
for (ground_param, pddl_param) in zip(mod.parameters, mod.parent.parameters):
sas_params.append((pddl_param.name, ground_param.type, ground_param.name))
grounding_modules.append(sas_tasks.SASGroundingModule(mod.modulecall, sas_params, module_groundings_to_sas[mod.toModuleCall()][0]))
return sas_tasks.SASTask(variables, init, goal, operators,
temp_operators, axioms, sas_num_axioms, comp_axioms[1], oplinit, objects, condition_modules, effect_modules, cost_modules, grounding_modules,
sas_tasks.SASTranslation(strips_to_sas), module_inits, module_exits, subplan_generators,
init_constant_predicates, init_constant_numerics)
def recover_stream_plan(evaluations, goal_expression, domain, stream_results, action_plan, negative,
unit_costs, optimize=True):
import pddl_to_prolog
import build_model
import pddl
import axiom_rules
import instantiate
# Universally quantified conditions are converted into negative axioms
# Existentially quantified conditions are made additional preconditions
# Universally quantified effects are instantiated by doing the cartesian produce of types (slow)
# Added effects cancel out removed effects
opt_evaluations = evaluations_from_stream_plan(evaluations, stream_results)
opt_task = task_from_domain_problem(domain, get_problem(opt_evaluations, goal_expression, domain, unit_costs))
real_task = task_from_domain_problem(domain, get_problem(evaluations, goal_expression, domain, unit_costs))
function_assignments = {fact.fluent: fact.expression for fact in opt_task.init # init_facts
if isinstance(fact, pddl.f_expression.FunctionAssignment)}
type_to_objects = instantiate.get_objects_by_type(opt_task.objects, opt_task.types)
results_from_head = get_results_from_head(opt_evaluations)
action_instances = []
for name, args in action_plan: # TODO: negative atoms in actions
candidates = []
for action in opt_task.actions:
if action.name != name:
continue
if len(action.parameters) != len(args):
raise NotImplementedError('Existential quantifiers are not currently '
'supported in preconditions: {}'.format(name))
variable_mapping = {p.name: a for p, a in zip(action.parameters, args)}
instance = action.instantiate(variable_mapping, set(),
MockSet(), type_to_objects,
opt_task.use_min_cost_metric, function_assignments)
assert (instance is not None)
candidates.append(((action, args), instance))
if not candidates:
raise RuntimeError('Could not find an applicable action {}'.format(name))
action_instances.append(candidates)
action_instances.append([(None, get_goal_instance(opt_task.goal))])
axioms_from_name = get_derived_predicates(opt_task.axioms)
negative_from_name = {n.name: n for n in negative}
opt_task.actions = []
opt_state = set(opt_task.init)
real_state = set(real_task.init)
preimage_plan = []
function_plan = set()
for layer in action_instances:
for pair, instance in layer:
nonderived_preconditions = [l for l in instance.precondition if l.predicate not in axioms_from_name]
#nonderived_preconditions = instance.precondition
if not conditions_hold(opt_state, nonderived_preconditions):
continue
opt_task.init = opt_state
original_axioms = opt_task.axioms
axiom_from_action = get_necessary_axioms(instance, original_axioms, negative_from_name)
opt_task.axioms = []
opt_task.actions = axiom_from_action.keys()
# TODO: maybe it would just be better to drop the negative throughout this process until this end
with Verbose(False):
model = build_model.compute_model(pddl_to_prolog.translate(opt_task)) # Changes based on init
opt_task.axioms = original_axioms
opt_facts = instantiate.get_fluent_facts(opt_task, model) | (opt_state - real_state)
mock_fluent = MockSet(lambda item: (item.predicate in negative_from_name) or
(item in opt_facts))
instantiated_axioms = instantiate_necessary_axioms(model, real_state, mock_fluent, axiom_from_action)
with Verbose(False):
helpful_axioms, axiom_init, _ = axiom_rules.handle_axioms([instance], instantiated_axioms, [])
axiom_from_atom = get_achieving_axioms(opt_state, helpful_axioms, axiom_init, negative_from_name)
axiom_plan = [] # Could always add all conditions
extract_axioms(axiom_from_atom, instance.precondition, axiom_plan)
# TODO: test if no derived solution
# TODO: compute required stream facts in a forward way and allow opt facts that are already known required
for effects in [instance.add_effects, instance.del_effects]:
for i, (conditions, effect) in enumerate(effects[::-1]):
if any(c.predicate in axioms_from_name for c in conditions):
raise NotImplementedError('Conditional effects cannot currently involve derived predicates')
if conditions_hold(real_state, conditions):
# Holds in real state
effects[i] = ([], effect)
elif not conditions_hold(opt_state, conditions):
# Does not hold in optimistic state
effects.pop(i)
else:
# TODO: handle more general case where can choose to achieve particular conditional effects
raise NotImplementedError('Conditional effects cannot currently involve certified predicates')
#if any(conditions for conditions, _ in instance.add_effects + instance.del_effects):
# raise NotImplementedError('Conditional effects are not currently supported: {}'.format(instance.name))
# TODO: add axiom init to reset state?
apply_action(opt_state, instance)
apply_action(real_state, instance)
preimage_plan.extend(axiom_plan + [instance])
if not unit_costs and (pair is not None):
function_plan.update(extract_function_results(results_from_head, *pair))
break
else:
raise RuntimeError('No action instances are applicable')
preimage = plan_preimage(preimage_plan, set())
preimage -= set(real_task.init)
negative_preimage = set(filter(lambda a: a.predicate in negative_from_name, preimage))
preimage -= negative_preimage
# visualize_constraints(map(fact_from_fd, preimage))
# TODO: prune with rules
# TODO: linearization that takes into account satisfied goals at each level
# TODO: can optimize for all streams & axioms all at once
for literal in negative_preimage:
negative = negative_from_name[literal.predicate]
instance = negative.get_instance(map(obj_from_pddl, literal.args))
value = not literal.negated
if instance.enumerated:
assert (instance.value == value)
else:
function_plan.add(PredicateResult(instance, value, opt_index=instance.opt_index))
node_from_atom = get_achieving_streams(evaluations, stream_results)
preimage_facts = list(map(fact_from_fd, filter(lambda l: not l.negated, preimage)))
stream_plan = []
extract_stream_plan(node_from_atom, preimage_facts, stream_plan)
if not optimize: # TODO: detect this based on unique or not
return stream_plan + list(function_plan)
# TODO: search in space of partially ordered plans
# TODO: local optimization - remove one and see if feasible
reschedule_problem = get_problem(evaluations, And(*preimage_facts), domain, unit_costs=True)
reschedule_task = task_from_domain_problem(domain, reschedule_problem)
reschedule_task.actions, stream_result_from_name = get_stream_actions(stream_results)
new_plan, _ = solve_from_task(reschedule_task, planner='max-astar', debug=False)
# TODO: investigate admissible heuristics
if new_plan is None:
return stream_plan + list(function_plan)
new_stream_plan = [stream_result_from_name[name] for name, _ in new_plan]
return new_stream_plan + list(function_plan)
def translate_task(strips_to_sas, ranges, translation_key,
numeric_strips_to_sas, num_count, mutex_dict, mutex_ranges,
mutex_key, init, num_init, goal_list, global_constraint,
actions, axioms, num_axioms, num_axioms_by_layer,
num_axiom_map, const_num_axioms, metric, implied_facts,
init_constant_predicates, init_constant_numerics):
with timers.timing("Processing axioms", block=True):
axioms, axiom_init, axiom_layer_dict = axiom_rules.handle_axioms(
actions, axioms, goal_list, global_constraint)
init = init + axiom_init
if options.dump_task:
# Remove init facts that don't occur in strips_to_sas: they're constant.
nonconstant_init = filter(strips_to_sas.get, init)
dump_task(nonconstant_init, goal_list, actions, axioms,
axiom_layer_dict)
init_values = [rang - 1 for rang in ranges]
# Closed World Assumption: Initialize to "range - 1" == Nothing.
for fact in init:
pairs = strips_to_sas.get(fact, []) # empty for static init facts
for var, val in pairs:
curr_val = init_values[var]
if curr_val != ranges[var] - 1 and curr_val != val:
assert False, "Inconsistent init facts! [fact = %s]" % fact
init_values[var] = val
comparison_axioms = [{}, []]
goal_dict_list = translate_strips_conditions(goal_list, strips_to_sas,
ranges, numeric_strips_to_sas,
mutex_dict, mutex_ranges,
comparison_axioms)
global_constraint_dict_list = translate_strips_conditions(
[global_constraint], strips_to_sas, ranges, numeric_strips_to_sas,
mutex_dict, mutex_ranges, comparison_axioms)
# print("goal_dict_list = %s" % goal_dict_list)
# print("comparison_axioms = %s" %comparison_axioms)
if goal_dict_list is None:
# "None" is a signal that the goal_list is unreachable because it
# violates a mutex.
return unsolvable_sas_task("Goal violates a mutex")
assert len(goal_dict_list) == 1, "Negative goal not supported"
## we could substitute the negative goal literal in
## normalize.substitute_complicated_goal, using an axiom. We currently
## don't do this, because we don't run into this assertion, if the
## negative goal is part of finite domain variable with only two
## values, which is most of the time the case, and hence refrain from
## introducing axioms (that are not supported by all heuristics)
goal_pairs = list(goal_dict_list[0].items())
if not goal_pairs:
return solvable_sas_task("Empty goal")
sas_goal = sas_tasks.SASGoal(goal_pairs)
assert len(
global_constraint_dict_list
) == 1 # the key is the axiom fluent, the value (0) the evaluation to true
num_init_values = [0.0
] * num_count # initialize numeric varialbes with 0.0
# if DEBUG:
# print("Strips-to-sas dict is")
# for entry in strips_to_sas:
# print("%s -> %s"%(entry, strips_to_sas[entry]))
# print("Numeric Strips-to-sas dict is")
# for entry in numeric_strips_to_sas:
# print("%s -> %s"%(entry,numeric_strips_to_sas[entry]))
relevant_numeric = []
for fact in num_init:
var = numeric_strips_to_sas.get(fact.fluent, -1)
if var > -1:
val = fact.expression.value
num_init_values[var] = val
if fact.fluent.ntype == 'R':
# the corresponding numeric variable is "regular" and therefore relevant
relevant_numeric.append(var)
operators = translate_strips_operators(actions, strips_to_sas, ranges,
numeric_strips_to_sas, mutex_dict,
mutex_ranges, implied_facts,
comparison_axioms, num_init_values,
relevant_numeric)
axioms = translate_strips_axioms(axioms, strips_to_sas, ranges,
numeric_strips_to_sas, mutex_dict,
mutex_ranges, comparison_axioms)
sas_num_axioms = [
translate_numeric_axiom(axiom, strips_to_sas, numeric_strips_to_sas)
for axiom in num_axioms
if axiom not in const_num_axioms and axiom.effect not in num_axiom_map
]
axiom_layers = [-1] * len(ranges) # default axiom layer is -1
## each numeric axiom gets its own layer (a wish of a colleague for
## knowledge compilation or search. If you use only the translator,
## you can change this)
num_axiom_layers = [-1] * num_count
num_axiom_layer = 0
for layer in num_axioms_by_layer:
num_axioms_by_layer[layer].sort(lambda x, y: cmp(x.name, y.name))
for axiom in num_axioms_by_layer[layer]:
if axiom.effect not in num_axiom_map:
var = numeric_strips_to_sas[axiom.effect]
if layer == -1:
num_axiom_layers[var] = -1
else:
num_axiom_layers[var] = num_axiom_layer
num_axiom_layer += 1
# print("comparison_axioms = %s" %comparison_axioms)
comp_axiom_init = [2] * len(
comparison_axioms[1]
) # initializing comparison axioms with value 3 (none of those)
init_values.extend(comp_axiom_init) #
for axiom in comparison_axioms[1]:
axiom_layers[axiom.effect] = num_axiom_layer
for atom, layer in axiom_layer_dict.iteritems():
assert layer >= 0
[(var, val)] = strips_to_sas[atom]
axiom_layers[var] = layer + num_axiom_layer + 1
add_key_to_comp_axioms(comparison_axioms, translation_key)
variables = sas_tasks.SASVariables(ranges, axiom_layers, translation_key,
num_axiom_layer)
num_variables = [-1] * num_count
num_var_types = ['U'] * num_count # variable type is unknown
for entry in numeric_strips_to_sas:
num_variables[numeric_strips_to_sas[entry]] = entry
num_var_types[numeric_strips_to_sas[entry]] = entry.ntype
assert num_count == len(num_variables), "%d nc <-> variables %d" % (
num_count, len(num_variables))
numeric_variables = sas_tasks.SASNumericVariables(num_variables,
num_axiom_layers,
num_var_types)
mutexes = [sas_tasks.SASMutexGroup(group) for group in mutex_key]
for axiom in const_num_axioms:
# print("Axiom = %s" % axiom)
# print("Axiom.effect = %s" % axiom.effect)
# print("corresponding variable = %s" % numeric_strips_to_sas.get(axiom.effect))
var = numeric_strips_to_sas.get(axiom.effect)
val = axiom.parts[0].value
num_init_values[var] = val
sas_init = sas_tasks.SASInit(init_values, num_init_values)
# print("SASInit is")
# sas_init.dump()
# look up metric fluent
if metric[1] == -1:
# minimize unit cost, no metric fluent specified
assert metric[0] == '<'
sas_metric = metric
else:
assert metric[
1] in numeric_strips_to_sas, "Metric fluent %s missing in strips_to_sas_dict" % metric[
1]
# look up (possibly derived) metric fluent to be optimized
sas_metric = (metric[0], numeric_strips_to_sas[metric[1]])
# print ("debug check metric: metric=")
# print (metric)
# print ("sas_metric fluent %d" % sas_metric[1])
# print ("Returning task with global constraint = ",global_constraint_dict_list[0].items()[0])
return sas_tasks.SASTask(variables, numeric_variables, mutexes, sas_init,
sas_goal, operators, axioms, comparison_axioms[1],
sas_num_axioms,
global_constraint_dict_list[0].items()[0],
sas_metric, init_constant_predicates,
init_constant_numerics)
def translate_task(strips_to_sas, ranges, init, goals, actions,
durative_actions, axioms, num_axioms, num_axioms_by_layer,
max_num_layer, num_axiom_map, const_num_axioms):
axioms, axiom_init, axiom_layer_dict = axiom_rules.handle_axioms(
actions, durative_actions, axioms, goals)
init = init + axiom_init
comp_axioms = [{},[]]
goal_pairs = translate_strips_conditions(goals, strips_to_sas, ranges, comp_axioms).items()
goal = sas_tasks.SASGoal(goal_pairs)
operators = translate_strips_operators(actions, strips_to_sas, ranges, comp_axioms)
temp_operators = translate_temporal_strips_operators(durative_actions,
strips_to_sas, ranges, comp_axioms)
axioms = translate_strips_axioms(axioms, strips_to_sas, ranges, comp_axioms)
sas_num_axioms = [translate_numeric_axiom(axiom,strips_to_sas) for axiom in num_axioms
if axiom not in const_num_axioms and
axiom.effect not in num_axiom_map]
axiom_layers = [-1] * len(ranges)
## each numeric axiom gets its own layer (a wish of a colleague for
## knowledge compilation or search. If you use only the translator,
## you can change this)
num_axiom_layer = 0
for layer in num_axioms_by_layer:
num_axioms_by_layer[layer].sort(lambda x,y: cmp(x.name,y.name))
for axiom in num_axioms_by_layer[layer]:
if axiom.effect not in num_axiom_map:
[(var,val)] = strips_to_sas[axiom.effect]
if layer == -1:
axiom_layers[var] = -1
else:
axiom_layers[var] = num_axiom_layer
num_axiom_layer += 1
for axiom in comp_axioms[1]:
axiom_layers[axiom.effect] = num_axiom_layer
for atom, layer in axiom_layer_dict.iteritems():
assert layer >= 0
[(var, val)] = strips_to_sas[atom]
axiom_layers[var] = layer + num_axiom_layer + 1
variables = sas_tasks.SASVariables(ranges, axiom_layers)
init_values = [rang - 1 for rang in ranges]
# Closed World Assumption: Initialize to "range - 1" == Nothing.
for fact in init:
if isinstance(fact,pddl.Atom):
pairs = strips_to_sas.get(fact, []) # empty for static init facts
for var, val in pairs:
assert init_values[var] == ranges[var] - 1, "Inconsistent init facts!"
init_values[var] = val
else: # isinstance(fact,pddl.FunctionAssignment)
pairs = strips_to_sas.get(fact.fluent,[]) #empty for constant functions
for (var,dummy) in pairs:
val = fact.expression.value
assert init_values[var] == ranges[var] - 1, "Inconsistent init facts!"
init_values[var]=val
for axiom in const_num_axioms:
var = strips_to_sas.get(axiom.effect)[0][0]
val = axiom.parts[0].value
init_values[var]=val
init = sas_tasks.SASInit(init_values)
return sas_tasks.SASTask(variables, init, goal, operators,
temp_operators, axioms, sas_num_axioms, comp_axioms[1])