def solve(self, *args, **kwargs):
# set external prev/1
for atom in self.last_prev:
self.control.assign_external(clingo.Function(PREV, atom), False)
for atom in self.prev:
self.control.assign_external(clingo.Function(PREV, atom), True)
# set external first/0
if self.calls == 0:
self.control.assign_external(clingo.Function(FIRST, []), True)
elif self.calls == 1:
self.control.release_external(clingo.Function(FIRST, []))
# add constraint for last model
if self.calls == 0:
self.backend.__enter__()
if self.calls > 0:
body = self.model + [-a for a in self.atoms if a not in self.model]
self.backend.add_rule([], body, False)
# if calls == 1 and optimization: add constraint
if self.calls == 1 and self.cost != []:
# just in case:
if len(self.cost) != len(self.minimization):
raise Exception("ERROR (multiclingo): implementation error")
# iterate over levels adding a weight constraint
for idx, cost in enumerate(self.cost):
level = self.minimization[idx]
self.backend.add_weight_rule([], cost+1, level, False)
total = sum([abs(w) for _, w in level])
body = [(-l,w) for l, w in level]
self.backend.add_weight_rule([], total-cost+1, body, False)
# solve and return result
self.result = UNSATISFIABLE
self.calls += 1
self.control.solve(on_model = self.on_model, *args, **kwargs)
return self.result
def test_function(self):
nc = noclingo.Function("atest")
c = clingo.Function("atest")
self.assertEqual(str(nc), str(c))
self.assertEqual(nc.type, noclingo.SymbolType.Function)
nc1 = noclingo.Function("aaaa")
nc2 = noclingo.String("bbb")
nc3 = noclingo.Function("ccc", [noclingo.Number(10)])
c1 = clingo.Function("aaaa")
c2 = clingo.String("bbb")
c3 = clingo.Function("ccc", [clingo.Number(10)])
nc = noclingo.Function("atest", [nc1, nc2, nc3])
c = clingo.Function("atest", [c1, c2, c3])
self.assertEqual(str(nc), str(c))
self.assertEqual(nc.name, c.name)
self.assertEqual(nc.name, "atest")
self.assertEqual(len(nc.arguments), len(c.arguments))
self.assertEqual(nc.arguments[0].name, c.arguments[0].name)
self.assertEqual(nc.arguments[0].name, "aaaa")
self.assertEqual(nc.arguments[1].string, c.arguments[1].string)
nc4 = noclingo.Function("ccc", [noclingo.Number(10)], False)
c4 = clingo.Function("ccc", [clingo.Number(10)], False)
self.assertEqual(str(nc4), str(c4))
self.assertEqual(nc4.positive, c4.positive)
self.assertEqual(nc4.negative, c4.negative)
def setStep(self, index):
if self.debug:
print "Setting horizon to ", index
oldqu = self.horizon
if self.optimize:
self.control.assign_external(clingo.Function("utility", [oldqu]),
False)
if not index in self.transitions:
if self.debug:
print "Grounding transition and utility ", index
self.control.ground([("transition", [index]),
("utility", [index])])
self.transitions.append(index)
self.control.assign_external(clingo.Function("utility", [index]),
True)
else:
self.control.assign_external(clingo.Function("query", [oldqu]),
False)
if not index in self.transitions:
if self.debug:
print "Grounding transition and query ", index
self.control.ground([("transition", [index]),
("query", [index])])
self.transitions.append(index)
self.control.assign_external(clingo.Function("query", [index]),
True)
self.horizon = index
def test_tuple(self):
nc1 = noclingo.Function("aaaa")
nc2 = noclingo.String("bbb")
c1 = clingo.Function("aaaa")
c2 = clingo.String("bbb")
nc = noclingo.Function("", [nc1, nc2])
c = clingo.Function("", [c1, c2])
def setUp(self):
self.inf = clingo.Infimum
self.sup = clingo.Supremum
self.s1 = clingo.String("aaaa")
self.s2 = clingo.String("bbbb")
self.n1 = clingo.Number(24)
self.n2 = clingo.Number(60)
self.f1 = clingo.Function("",[self.s1, self.s2])
self.f2 = clingo.Function("func1", [self.n1])
self.f3 = clingo.Function("func2", [self.n2, self.f1])
self.f4 = clingo.Function("func1", [], False)
self.alls = [self.inf, self.sup, self.s1, self.s2, self.n1, \
self.n2, self.f1, self.f2, self.f3, self.f4 ]
self.str_inf = '{"clingo.SymbolType": "Infimum"}'
self.str_sup = '{"clingo.SymbolType": "Supremum"}'
self.str_s1 = '{"clingo.SymbolType": "String", "string": "aaaa"}'
self.str_s2 = '{"clingo.SymbolType": "String", "string": "bbbb"}'
self.str_n1 = '{"clingo.SymbolType": "Number", "number": 24}'
self.str_n2 = '{"clingo.SymbolType": "Number", "number": 60}'
self.str_f1 = '{"clingo.SymbolType": "Function", "name": "", "arguments": [' \
+ self.str_s1 + ', ' + self.str_s2 + '], "positive": true}'
self.str_f2 = '{"clingo.SymbolType": "Function", "name": "func1", "arguments": [' \
+ self.str_n1 + '], "positive": true}'
self.str_f3 = '{"clingo.SymbolType": "Function", "name": "func2", "arguments": [' \
+ self.str_n2 + ', ' + self.str_f1 + '], "positive": true}'
self.str_f4 = '{"clingo.SymbolType": "Function", "name": "func1", "arguments": []' \
+ ', "positive": false}'
self.str_alls = '[' + self.str_inf + ', ' + self.str_sup + ', ' + \
self.str_s1 + ', ' + self.str_s2 + ', ' + self.str_n1 + ', ' +\
self.str_n2 + ', ' + self.str_f1 + ', ' + self.str_f2 + ', ' +\
self.str_f3 + ', ' + self.str_f4 + ']'
def start(self, on_finish):
if self.ret is not None and not self.ret.unknown():
self.k = self.k + 1
self.prg.ground([("sleep", [self.k])])
self.prg.release_external(clingo.Function("sleep", [self.k-1]))
self.prg.assign_external(clingo.Function("sleep", [self.k]), True)
self.future = self.prg.solve(on_model=self.on_model, on_finish=on_finish, async=True)
def incmode():
#dlp = DynamicLogicProgramBackend(["example.lp"], clingo_options=sys.argv[1:])
#dlp = DynamicLogicProgramBackend(["example.lp"], clingo_options=sys.argv[1:])
dlp = DynamicLogicProgramBackendClingoPre(["example-clingo-pre.lp"], clingo_options=sys.argv[1:])
dlp.start()
print(dlp); return
# loop
step, ret = 1, None
while True:
if step > 2: return
dlp.release_external(clingo.Function("query",[step-1]))
dlp.ground(1)
dlp.assign_external(clingo.Function("query",[step]), True)
with dlp.control.solve(assumptions=dlp.get_assumptions(), yield_ = True) as handle:
for m in handle:
print("Step: {}\n{}\nSATISFIABLE".format(step, " ".join(
["{}:{}".format(x,y) for x,y in dlp.get_answer(m, step)]
)))
return
print("Step: {}\nUNSATISFIABLE".format(step))
step += 1
def parallel_plan(self):
"""Planning function for the parallel mode, based on clingo incremental mode
Here no additional inputs are needed"""
prg = clingo.Control(arguments=["-Wnone"])
prg.load(self.encoding)
prg.load(self.instance)
imin = self.get(prg.get_const("imin"), clingo.Number(0))
imax = prg.get_const("imax")
istop = self.get(prg.get_const("istop"), clingo.String("SAT"))
step, ret = 0, None
while ((imax is None or step < imax.number)
and (step == 0 or step < imin.number or
((istop.string == "SAT" and not ret.satisfiable) or
(istop.string == "UNSAT" and not ret.unsatisfiable) or
(istop.string == "UNKNOWN" and not ret.unknown)))):
parts = []
parts.append(("check", [step]))
if step > 0:
prg.release_external(clingo.Function("query", [step - 1]))
parts.append(("step", [step]))
prg.cleanup()
else:
parts.append(("base", []))
prg.ground(parts)
prg.assign_external(clingo.Function("query", [step]), True)
ret, step = prg.solve(on_model=self.get_parallel_plan), step + 1
# save plan length for benchmark output
self.plan_length = step - 1
def main(prg):
imin = get(prg.get_const("imin"), 1)
imax = get(prg.get_const("imax"), 100)
istop = get(prg.get_const("istop"), "SAT")
step = 0
prg.ground([("base", [])])
ret = prg.solve(on_model = __on_model)
while ((imax is None or step < imax) and
(step == 0 or step < imin or (
(istop == "SAT" and ret != clingo.SolveResult.satisfiable) or
(istop == "UNSAT" and ret != clingo.SolveResult.unsatisfiable) or
(istop == "UNKNOWN" and ret != clingo.SolveResult.unknown)))):
# print "=== Add cumulative rules to program"
print "=== Set cumulative(" + str(step) + ")"
prg.ground([("step", [step])])
# print "=== Set external query(" + str(step) + ") to be true "
prg.assign_external(clingo.Function("query", [step]), True)
ret = prg.solve(on_model = __on_model)
# print "=== Release external query(" + str(step) + ") "
prg.release_external(clingo.Function("query", [step]))
if ret == clingo.SolveResult.SAT:
print "=== Found solution "
break
step = step+1
def encode_BooleanFunction(self, n, f, ensure_dnf=True):
def clauses_of_dnf(f):
if f == self.ba.FALSE:
return [False]
if f == self.ba.TRUE:
return [True]
if isinstance(f, boolean.OR):
return f.args
else:
return [f]
def literals_of_clause(c):
def make_literal(l):
if isinstance(l, boolean.NOT):
return (l.args[0].obj, -1)
else:
return (l.obj, 1)
lits = c.args if isinstance(c, boolean.AND) else [c]
return map(make_literal, lits)
facts = []
if ensure_dnf:
f = self.ba.dnf(f).simplify()
for cid, c in enumerate(clauses_of_dnf(f)):
if isinstance(c, bool):
facts.append(clingo.Function("constant", symbols(n, s2v(c))))
else:
for m, v in literals_of_clause(c):
facts.append(
clingo.Function("clause", symbols(n, cid + 1, m, v)))
return facts
def pkn_to_facts(pkn, maxclause=None, allow_skipping_nodes=False):
facts = []
if not allow_skipping_nodes:
facts.append(asp.Function("nbnode", symbols(len(pkn.nodes()))))
for n in pkn.nodes():
facts.append(asp.Function("node", symbols(n)))
else:
facts.append("nbnode(NB) :- NB = #count{N: node(N)}")
for n in pkn.nodes():
facts.append("{{{}}}".format(asp.Function("node", symbols(n))))
for (orig, dest, data) in pkn.edges(data=True):
if data["sign"] in ["ukn", "?", "0", 0]:
args = symbols(orig, dest)
f = "in({},{},(-1;1))".format(*args)
facts.append(f)
else:
ds = data["sign"]
if ds in ["-", "+"]:
ds += "1"
s = int(ds)
facts.append(asp.Function("in", symbols(orig, dest, s)))
def bounded_nb_clauses(d):
nbc = nb_clauses(d)
if maxclause:
nbc = min(maxclause, nbc)
return nbc
for n, i in pkn.in_degree(pkn.nodes()):
facts.append(asp.Function("maxC", symbols(n, bounded_nb_clauses(i))))
return facts
def process_model(self) -> bool:
found_model = False
if self.model:
found_model = True
for atom in self.model:
if atom.name == "chooseShelf":
self.shelf = atom.arguments[0].number
elif (atom.name == "putdown"
and self.domain == "b") or (atom.name == "pickup"):
self.plan_length = atom.arguments[1].number
elif atom.name == "deliver" and self.domain == "b":
if self.plan_length < atom.arguments[3].number:
self.plan_length = atom.arguments[3].number
if not found_model:
self.plan_length = -1
self.next_action = clingo.Function("", [])
if self.shelf == -1:
if self.external:
self.prg.assign_external(
clingo.Function("order", [
self.order[1], self.order[2], self.order[0],
self.id
]), False)
for shelf in self.available_shelves:
self.prg.assign_external(
clingo.Function("available", [shelf]), False)
self.available_shelves = []
return found_model
def apply(self, qexpr, *args, **kwargs):
"""Applies query."""
Query.ext_atom = self._gen_ext_atom()
Query.session_step = self._session.step
prg = self._session.state.prg
enc_name = "query_" + str(self._session.step + 1)
parse_result = QueryParser.queryexp.parseString(qexpr, True)
parse_result_str = str(parse_result).strip("[]")
parse_result_str = re.sub(
r'\(\S*\)', '',
parse_result_str.split()[0]
) + " " + parse_result_str.split(
' ', 1
)[1] # TODO: Hack to flatten top-level head. Adjust semantic action/parser to omit vars in body of top-level head
debug(parse_result_str, "Query.apply: parse_result")
prg.add(enc_name, [],
parse_result_str) # equivalent rules for query expr
prg.add(enc_name, [], "#external {0}.".format(
Query.ext_atom)) # external atom switch
prg.ground([(enc_name, [])])
prg.assign_external(clingo.Function(Query.ext_atom), True)
query_head = clingo.Function(parse_result_str.split()[0])
debug(query_head, "Query.apply: query_head")
assumptions = list(
self._session.state.assumptions
) # copy of assumptions to add query head atom for solving
assumptions.append((query_head, True))
output_on_model = [] # output returned by on_model
self._session.state.prg.solve(
on_model(self._shared_data['show_preds'], output_on_model),
assumptions)
self.prnt('\n'.join(output_on_model) + '\n')
prg.release_external(clingo.Function(Query.ext_atom))
self._update_session_log("query", "qexpr", qexpr)
def test_comparison_ops(self):
nc1 = noclingo.Number(34)
nc2 = noclingo.Number(43)
self.assertTrue(nc1 <= nc2)
self.assertTrue(nc1 < nc2)
self.assertTrue(nc2 >= nc1)
self.assertTrue(nc2 > nc1)
nc3 = noclingo.String("abcd")
nc4 = noclingo.String("bcde")
self.assertTrue(nc3 <= nc4)
self.assertTrue(nc3 < nc4)
self.assertTrue(nc4 >= nc3)
self.assertTrue(nc4 > nc3)
nc5 = noclingo.Function("abc", [noclingo.Number(45)])
nc6 = noclingo.Function("abc", [noclingo.String("45")])
c5 = clingo.Function("abc", [clingo.Number(45)])
c6 = clingo.Function("abc", [clingo.String("45")])
if c5 < c6: self.assertTrue(nc5 < nc6)
else: self.assertTrue(nc5 > nc6)
nc7 = noclingo.Function(
"abc",
[noclingo.String("45"), noclingo.Number(5)])
self.assertTrue(nc6 < nc7)
if noclingo.SymbolType.Number < noclingo.SymbolType.String:
self.assertTrue(nc1 < nc3)
else:
self.assertTrue(nc1 > nc3)
def solutions(self, on_model, on_model_weight=None, limit=0,
force_weight=None):
control = self.default_control("0", "--project")
do_subsets = self.opts.family == "subset" \
or (self.opts.family =="mincard" and self.opts.mincard_tolerance)
minsize = None
self.setup_opt(control)
control.load(aspf("show.lp"))
control.ground([("base", [])])
start = time.time()
if self.nodataset:
force_weight = 0
if force_weight is None:
control.assign_external(clingo.Function("tolerance"),False)
dbg("# start initial solving")
opt = []
res = control.solve(on_model=lambda model: opt.append(model.cost))
dbg("# initial solve took %s" % (time.time()-start))
optimizations = opt.pop()
dbg("# optimizations = %s" % optimizations)
weight = optimizations[0]
if self.do_mincard:
minsize = optimizations[1]
if weight > 0 and on_model_weight is not None:
dbg("# model has weight, changing enumeration mode")
for sample in self.solution_samples():
on_model_weight(sample)
return
control.assign_external(clingo.Function("tolerance"),True)
else:
weight = force_weight
control.assign_external(clingo.Function("tolerance"),True)
dbg("# force weight = %d" % weight)
self.setup_weight(control, weight)
self.setup_card(control, minsize)
control.configuration.solve.opt_mode = "ignore"
control.configuration.solve.models = limit
control.configuration.solve.project = 1
if do_subsets:
control.configuration.solve.enum_mode = "domRec"
control.configuration.solver[0].heuristic = "Domain"
control.configuration.solver[0].dom_mod = "5,16"
start = time.time()
dbg("# begin enumeration")
res = control.solve(on_model=on_model)
dbg("# enumeration took %s" % (time.time()-start))
def get_external(self, m1, m2):
external = self.externals.get((m1, m2))
if external is None:
f1 = clingo.Function(self.model_str, [m1])
f2 = clingo.Function(self.model_str, [m2])
external = clingo.Function(self.volatile_str, [f1, f2])
self.externals[(m1, m2)] = external
return external
def encode_bind_cfg(self, cfg, obs, mutant=None):
args = (cfg, obs)
if mutant is not None:
self.load_template_bind_cfg_mutant()
args = args + (clingo.Function("mutant", symbols(mutant)), )
else:
self.load_template_bind_cfg()
return [clingo.Function("bind_cfg", symbols(*args))]
def visit_TheoryAtom(self, atom):
"""
Rewrites theory atoms related to temporal formulas.
An atom of form `&tel {...}` is rewritten to `&tel(k) {...}`, atoms of
form `&initial` and `&final` are rewritten to `__initial` and
`__final`, and atoms of form `&true` and `&false` are rewritten to
`#true` and `#false`.
"""
if atom.term.ast_type == _ast.ASTType.Function and len(
atom.term.arguments) == 0:
time = lambda loc: _ast.SymbolicTerm(
loc, _clingo.Function(_tf.g_time_parameter_name))
wrap = lambda loc, atom: _ast.Literal(
loc, _ast.Sign.DoubleNegation, atom) if self.__head else atom
if atom.term.name == "del":
if not self.__negation and not self.__constraint:
raise RuntimeError(
"dynamic formulas not supported in this context: {}".
format(_tf.str_location(atom.location)))
atom.term.arguments = [
_ast.SymbolicTerm(atom.term.location,
_clingo.Function("__t"))
]
elif atom.term.name == "tel":
if self.__head:
atom, rules = self.__head_transformer.transform(atom)
self.__aux_rules.extend(rules)
else:
if not self.__negation and not self.__constraint:
raise RuntimeError(
"temporal formulas not supported in this context: {}"
.format(_tf.str_location(atom.location)))
for element in atom.elements:
if len(element.terms) != 1:
raise RuntimeError(
"invalid temporal formula: {}".format(
_tf.str_location(atom.location)))
self.visit(element.condition)
atom.term = self.__term_transformer.visit(
atom.term, False, True, True, self.__max_shift)
elif atom.term.name == "initial":
atom = wrap(
atom.location,
_ast.SymbolicAtom(
_ast.Function(atom.location, "__initial",
[time(atom.location)], False)))
elif atom.term.name == "final":
atom = wrap(
atom.location,
_ast.SymbolicAtom(
_ast.Function(atom.location, "__final",
[time(atom.location)], False)))
elif atom.term.name == "true":
atom = wrap(atom.location, _ast.BooleanConstant(True))
elif atom.term.name == "false":
atom = wrap(atom.location, _ast.BooleanConstant(False))
return atom
def __next(self):
assert (self.__horizon < 30)
self.__prg.assign_external(
clingo.Function("horizon", [self.__horizon]), False)
self.__horizon += 1
self.__prg.ground([("trans", [self.__horizon]),
("check", [self.__horizon]),
("state", [self.__horizon])])
self.__prg.assign_external(
clingo.Function("horizon", [self.__horizon]), True)
def test_symbol_generator(self):
csg = get_symbol_generator(SymbolMode.CLINGO)
ncsg = get_symbol_generator(SymbolMode.NOCLINGO)
self.assertEqual(csg.mode, SymbolMode.CLINGO)
self.assertEqual(ncsg.mode, SymbolMode.NOCLINGO)
cli = clingo.Infimum
cls = clingo.Supremum
cl1 = clingo.Function("const")
cl2 = clingo.Number(3)
cl3 = clingo.String("No")
cl4 = clingo.Function("", [cl1, cl2])
cl5 = clingo.Function("f", [cl3, cl4, cl1], False)
ncli = noclingo.Infimum
ncls = noclingo.Supremum
ncl1 = noclingo.Function("const")
ncl2 = noclingo.Number(3)
ncl3 = noclingo.String("No")
ncl4 = noclingo.Function("", [ncl1, ncl2])
ncl5 = noclingo.Function("f", [ncl3, ncl4, ncl1], False)
csg_cli = csg.Infimum
csg_cls = csg.Supremum
csg_cl1 = csg.Function("const")
csg_cl2 = csg.Number(3)
csg_cl3 = csg.String("No")
csg_cl4 = csg.Function("", [cl1, cl2])
csg_cl5 = csg.Function("f", [cl3, cl4, cl1], False)
ncsg_ncli = ncsg.Infimum
ncsg_ncls = ncsg.Supremum
ncsg_ncl1 = ncsg.Function("const")
ncsg_ncl2 = ncsg.Number(3)
ncsg_ncl3 = ncsg.String("No")
ncsg_ncl4 = ncsg.Function("", [ncsg_ncl1, ncsg_ncl2])
ncsg_ncl5 = ncsg.Function("f", [ncsg_ncl3, ncsg_ncl4, ncsg_ncl1],
False)
self.assertEqual(cli, csg_cli)
self.assertEqual(cls, csg_cls)
self.assertEqual(cl1, csg_cl1)
self.assertEqual(cl2, csg_cl2)
self.assertEqual(cl3, csg_cl3)
self.assertEqual(cl4, csg_cl4)
self.assertEqual(cl5, csg_cl5)
self.assertEqual(ncli, ncsg_ncli)
self.assertEqual(ncls, ncsg_ncls)
self.assertEqual(ncl1, ncsg_ncl1)
self.assertEqual(ncl2, ncsg_ncl2)
self.assertEqual(ncl3, ncsg_ncl3)
self.assertEqual(ncl4, ncsg_ncl4)
self.assertEqual(ncl5, ncsg_ncl5)
def imain(prg, future_sigs, program_parts, on_model, imin = 0, imax = None, istop = "SAT"):
"""
Take a program object and runs the incremental main solving loop.
For each pair (name, arity) in future_sigs all atoms in the program base
with the time parameter referring to the future are set to false. For
example, given (p, 2) and atoms p(x,1) in step 0, the atom would p(x,1)
would be set to false via an assumption. In the following time steps, it
would not be set to False.
The list program_parts contains all program parts appearing in the program
in form of triples (root, name, range) where root is either "initial" (time
step 0), "always" (time steps >= 0), or "dynamic" (time steps > 0) and
range is a list of integers for which the part has to be grounded
backwards. Given range [0, 1] and root "always", at each iteration the
program part would be grounded at horizon and horizon-1. The latter only if
the horizon is greater than 0.
Arguments:
prg -- Control object holding the program.
future_sigs -- Signatures of predicates whose future incarnations have to
be set to False.
program_parts -- Program parts to ground.
imin -- Minimum number of iterations.
imax -- Maximum number of iterations.
istop -- When to stop.
"""
f = _ty.Theory()
step, ret = 0, None
while ((imax is None or step < imax) and
(step == 0 or step < imin or (
(istop == "SAT" and not ret.satisfiable) or
(istop == "UNSAT" and not ret.unsatisfiable) or
(istop == "UNKNOWN" and not ret.unknown)))):
parts = []
for root_name, part_name, rng in program_parts:
for i in rng:
if ((step - i >= 0 and root_name == "always") or
(step - i > 0 and root_name == "dynamic") or
(step - i == 0 and root_name == "initial")):
parts.append((part_name, [step - i, step]))
if step > 0:
prg.release_external(_clingo.Function("__final", [step-1]))
prg.cleanup()
prg.ground(parts)
f.translate(step, prg)
prg.assign_external(_clingo.Function("__final", [step]), True)
assumptions = []
for name, arity, positive in future_sigs:
for atom in prg.symbolic_atoms.by_signature(name, arity, positive):
if atom.symbol.arguments[-1].number > step:
assumptions.append(-atom.literal)
ret, step = prg.solve(on_model=lambda m: on_model(m, step), assumptions=assumptions), step+1
def release_order(self):
"""Deactivate external for current order"""
if self.external:
self.prg.assign_external(
clingo.Function(
"order",
[self.order[1], self.order[2], self.order[0], self.id]),
False)
for shelf in self.available_shelves:
self.prg.assign_external(clingo.Function("available", [shelf]),
False)
self.shelf = -1
def __init__(self, t):
super(PA_Functionc, self).__init__(t)
arg_fun_objects = []
try:
arg_fun_objects = list(arg.ggfun for arg in t[0][1])
except IndexError:
pass
if arg_fun_objects:
self.ggfun = clingo.Function(t[0][0], arg_fun_objects)
else:
self.ggfun = clingo.Function(t[0][0])
debug("ggfun " + type(self.ggfun).__name__ + " " + str(self.ggfun))
def term_to_symbol(self, term):
"""
Converts theory terms in the form of function symbols to clingo function symbols.
"""
if term.type == clingo.TheoryTermType.Function:
return clingo.Function(
term.name,
[self.term_to_symbol(arg) for arg in term.arguments])
if term.type == clingo.TheoryTermType.Symbol:
return clingo.Function(term.name)
if term.type == clingo.TheoryTermType.Number:
return clingo.Number(term.number)
raise RuntimeError("Incorrect Term Type")
def _evaluate_term(term):
# pylint: disable=too-many-return-statements
# tuples
if term.type == clingo.TheoryTermType.Tuple:
return clingo.Tuple(_evaluate_term(x) for x in term.arguments)
# functions and arithmetic operations
if term.type == clingo.TheoryTermType.Function:
# binary operations
if term.name in _BOP and len(term.arguments) == 2:
a = _evaluate_term(term.arguments[0])
b = _evaluate_term(term.arguments[1])
if a.type != clingo.SymbolType.Number or b.type != clingo.SymbolType.Number:
raise RuntimeError("Invalid Binary Operation")
if term.name in ("/", "\\") and b.number == 0:
raise RuntimeError("Division by Zero")
return clingo.Number(_BOP[term.name](a.number, b.number))
# unary operations
if term.name == "-" and len(term.arguments) == 1:
a = _evaluate_term(term.arguments[0])
if a.type == clingo.SymbolType.Number:
return clingo.Number(-a.number)
if a.type == clingo.SymbolType.Function and a.name:
return clingo.Function(a.name, a.arguments, not a.positive)
raise RuntimeError("Invalid Unary Operation")
# invalid operators
if term.name == ".." and len(term.arguments) == 2:
raise RuntimeError("Invalid Interval")
# functions
return clingo.Function(term.name,
(_evaluate_term(x) for x in term.arguments))
# constants
if term.type == clingo.TheoryTermType.Symbol:
return clingo.Function(term.name)
# numbers
if term.type == clingo.TheoryTermType.Number:
return clingo.Number(term.number)
raise RuntimeError("Invalid Syntax")
def handle_message(self, msg):
if msg == "interrupt":
self.state = SolveThread.STATE_IDLE
elif msg == "exit":
self.state = SolveThread.STATE_EXIT
elif msg == "less_pigeon_please":
self.prg.assign_external(clingo.Function("p"), False)
self.state = SolveThread.STATE_IDLE
elif msg == "more_pigeon_please":
self.prg.assign_external(clingo.Function("p"), True)
self.state = SolveThread.STATE_IDLE
elif msg == "solve":
self.state = SolveThread.STATE_SOLVE
else: raise(RuntimeError("unexpected message: " + msg))
def __on_model(self, model):
ass = self.prop.assignment(model.thread_id)
if ass is not None:
(opt, values) = ass
model.extend(
[clingo.Function('_lp_optimum', [clingo.String(str(opt))])])
for name in values:
model.extend([
clingo.Function('_lp_solution', [
clingo.Function(name, []),
clingo.String(str(values[name]))
])
])
def plan(self):
"""Planning function for the sequential mode, based on clingo incremental mode
Additionally adds atoms for which robot and for which orders a plan needs to be found,
and plans of all previous robots are added as input"""
prg = clingo.Control(arguments=["-Wnone"])
prg.load(self.encoding)
prg.load(self.instance)
# which robot is planning
prg.add("base", [],
"planning(robot(" + str(self.current_robot) + ")).")
# which orders need to be planned
for oid in self.assignment[self.current_robot]:
prg.add("base", [], "process(order(" + str(oid) + ")).")
prg.add("base", [], "planLength(" + str(self.plan_length) + ").")
# add plans of all previous robots
if self.current_robot != 1:
for i in range(1, self.current_robot):
for atom in self.plans[i]:
prg.add("base", [], str(atom) + ".")
# incremental solving
imin = self.get(prg.get_const("imin"), clingo.Number(0))
imax = prg.get_const("imax")
istop = self.get(prg.get_const("istop"), clingo.String("SAT"))
step, ret = 0, None
while ((imax is None or step < imax.number)
and (step == 0 or step < imin.number or
((istop.string == "SAT" and not ret.satisfiable) or
(istop.string == "UNSAT" and not ret.unsatisfiable) or
(istop.string == "UNKNOWN" and not ret.unknown)))):
parts = []
parts.append(("check", [step]))
if step > 0:
prg.release_external(clingo.Function("query", [step - 1]))
parts.append(("step", [step]))
prg.cleanup()
else:
parts.append(("base", []))
prg.ground(parts)
prg.assign_external(clingo.Function("query", [step]), True)
ret, step = prg.solve(on_model=self.get_plan), step + 1
if self.debug:
print(str(step - 1))
# save maximum plan length
if self.plan_length < step - 1:
self.plan_length = step - 1
def start(self, pos, target):
self.__assign = []
for (robot, x, y) in pos:
self.__last_position[robot] = (x, y)
self.__assign.append(
clingo.Function("pos", [clingo.Function(robot), x, y, 0]))
print target
self.__assign.append(
clingo.Function(
"target", [clingo.Function(target[0]), target[1], target[2]]))
for x in self.__assign:
self.__prg.assign_external(x, True)
self.__solution = None
self.__future = self.__prg.solve(on_model=self.__on_model, async=True)
def test_comparison_ops(self):
nc1 = noclingo.Number(34)
nc2 = noclingo.Number(43)
self.assertTrue(nc1 <= nc2)
self.assertTrue(nc1 < nc2)
self.assertTrue(nc2 >= nc1)
self.assertTrue(nc2 > nc1)
nc3 = noclingo.String("abcd")
nc4 = noclingo.String("bcde")
self.assertTrue(nc3 <= nc4)
self.assertTrue(nc3 < nc4)
self.assertTrue(nc4 >= nc3)
self.assertTrue(nc4 > nc3)
nc5 = noclingo.Function("abc", [noclingo.Number(45)])
nc6 = noclingo.Function("abc", [noclingo.String("45")])
c5 = clingo.Function("abc", [clingo.Number(45)])
c6 = clingo.Function("abc", [clingo.String("45")])
if c5 < c6: self.assertTrue(nc5 < nc6)
else: self.assertTrue(nc5 > nc6)
nc7 = noclingo.Function(
"abc",
[noclingo.String("45"), noclingo.Number(5)])
self.assertTrue(nc6 < nc7)
def compare_ordering(a, b):
if noclingo_to_clingo(a) < noclingo_to_clingo(b):
self.assertTrue(a < b)
elif noclingo_to_clingo(b) < noclingo_to_clingo(a):
self.assertTrue(b < a)
else:
self.assertEqual(a, b)
compare_ordering(noclingo.String("1"), noclingo.Number(2))
compare_ordering(noclingo.Number(1), noclingo.String("2"))
compare_ordering(noclingo.String("1"), noclingo.Function("2"))
compare_ordering(noclingo.Function("2"), noclingo.String("1"))
compare_ordering(noclingo.Number(1), noclingo.Function("2"))
compare_ordering(noclingo.Function("1"), noclingo.Number(2))
compare_ordering(noclingo.Infimum, noclingo.Supremum)
compare_ordering(noclingo.Supremum, noclingo.Infimum)
compare_ordering(noclingo.Infimum, noclingo.Number(2))
compare_ordering(noclingo.Infimum, noclingo.String("2"))
compare_ordering(noclingo.Infimum, noclingo.Function("2"))
compare_ordering(noclingo.Supremum, noclingo.Function("2"))
compare_ordering(noclingo.Supremum, noclingo.String("2"))
compare_ordering(noclingo.Supremum, noclingo.Number(2))