def synthesize_enumerated_streett(spec, use_cudd=False):
"""Return transducer enumerated as a graph.
@type spec: `tulip.spec.form.GRSpec`
@param use_cudd: efficient BDD computations with `dd.cudd`
@rtype: `networkx.DiGraph`
"""
aut = _grspec_to_automaton(spec)
sym.fill_blanks(aut)
bdd = _init_bdd(use_cudd)
aut.bdd = bdd
a = aut.build()
t0 = time.time()
z, yij, xijk = gr1.solve_streett_game(a)
t1 = time.time()
# unrealizable ?
if z == a.bdd.false:
return None
t = gr1.make_streett_transducer(z, yij, xijk, a)
t2 = time.time()
(u,) = t.action['sys']
care = _int_bounds(t)
g = enum.relation_to_graph(u, t, care_source=care,
care_target=care)
h = _strategy_to_state_annotated(g, a)
del u, yij, xijk, care
t3 = time.time()
log.info((
'Winning set computed in {win} sec.\n'
'Symbolic strategy computed in {sym} sec.\n'
'Strategy enumerated in {enu} sec.').format(
win=t1 - t0,
sym=t2 - t1,
enu=t3 - t2))
return h
def synthesize_enumerated_streett(spec, use_cudd=False):
"""Return transducer enumerated as a graph.
@type spec: `tulip.spec.form.GRSpec`
@param use_cudd: efficient BDD computations with `dd.cudd`
@rtype: `networkx.DiGraph`
"""
aut = _grspec_to_automaton(spec)
sym.fill_blanks(aut)
bdd = _init_bdd(use_cudd)
aut.bdd = bdd
a = aut.build()
assert a.action['sys'][0] != bdd.false
t0 = time.time()
z, yij, xijk = gr1.solve_streett_game(a)
t1 = time.time()
# unrealizable ?
if not gr1.is_realizable(z, a):
print('WARNING: unrealizable')
return None
t = gr1.make_streett_transducer(z, yij, xijk, a)
t2 = time.time()
(u, ) = t.action['sys']
assert u != bdd.false
g = enum.action_to_steps(t, qinit=spec.qinit)
h = _strategy_to_state_annotated(g, a)
del u, yij, xijk
t3 = time.time()
log.info(('Winning set computed in {win} sec.\n'
'Symbolic strategy computed in {sym} sec.\n'
'Strategy enumerated in {enu} sec.').format(win=t1 - t0,
sym=t2 - t1,
enu=t3 - t2))
return h
def generate_complement(aut_streett):
"""Return rabin automaton. This function is used to create complement automaton from given streett
@type aut_streett: `omega.symbolic.symbolic.Automaton`
@rtype: `omega.symbolic.symbolic.Automaton`
"""
aut_rabin = sym.Automaton()
aut_rabin.acceptance = 'Rabin(1)'
# Switch variable ownership
for var, d in aut_streett.vars.items():
d = d.copy()
owner = d['owner']
owner = 'env' if owner == 'sys' else 'sys'
d['owner'] = owner
aut_rabin.vars[var] = d
aut_rabin.init['env']=aut_streett.init['sys']
aut_rabin.init['sys']=aut_streett.init['env']
aut_rabin.action['env'] = aut_streett.action['sys']
aut_rabin.action['sys'] = aut_streett.action['env']
win = ['!({w})'.format(w=w) for w in aut_streett.win['<>[]']]
aut_rabin.win['[]<>'] = win
win = ['!({w})'.format(w=w) for w in aut_streett.win['[]<>']]
aut_rabin.win['<>[]'] = win
sym.fill_blanks(aut_rabin, rabin=True)
aut_rabin.qinit = '\E \E'
return aut_rabin
def is_circular(spec, use_cudd=False):
"""Return `True` if trivial winning set non-empty.
@type spec: `tulip.spec.form.GRSpec`
@param use_cudd: efficient BDD computations with `dd.cudd`
@rtype: `bool`
"""
aut = _grspec_to_automaton(spec)
sym.fill_blanks(aut)
bdd = _init_bdd(use_cudd)
aut.bdd = bdd
triv, t = gr1.trivial_winning_set(aut)
return triv != t.bdd.false
def is_circular(spec, use_cudd=False):
"""Return `True` if trivial winning set non-empty.
@type spec: `tulip.spec.form.GRSpec`
@param use_cudd: efficient BDD computations with `dd.cudd`
@rtype: `bool`
"""
aut = _grspec_to_automaton(spec)
sym.fill_blanks(aut)
bdd = _init_bdd(use_cudd)
aut.bdd = bdd
triv, t = gr1.trivial_winning_set(aut)
return triv != t.bdd.false
def is_realizable(spec, use_cudd=False):
"""Return `True` if, and only if, realizable.
See `synthesize_enumerated_streett` for more details.
"""
aut = _grspec_to_automaton(spec)
sym.fill_blanks(aut)
bdd = _init_bdd(use_cudd)
aut.bdd = bdd
a = aut.build()
t0 = time.time()
z, _, _ = gr1.solve_streett_game(a)
t1 = time.time()
return gr1.is_realizable(z, a)
def synthesize_environment(spec, use_cudd=False):
"""Return transducer enumerated as a graph.
@type spec: `tulip.spec.form.GRSpec`
@param use_cudd: efficient BDD computations with `dd.cudd`
@rtype: `networkx.DiGraph`
"""
aut = tulip.interfaces.omega._grspec_to_automaton(spec)
sym.fill_blanks(aut)
bdd = tulip.interfaces.omega._init_bdd(use_cudd)
aut.bdd = bdd
a = aut.build()
assert a.action['sys'][0] != bdd.false
z, yij, xijk = gr1.solve_streett_game(a)
if (not gr1.is_realizable(z, a)) :
aut_rabin = generate_complement(aut)
controller_rabin = synthesize_rabin_controller(aut_rabin)
h = tulip.interfaces.omega._strategy_to_state_annotated(controller_rabin, a)
return h;
else:
print("Warning: the automata is synthesizable")
# psi |= DSL.response(trig=b[i], react=f[i], owner='sys', aux='aux' + str(i+1))
#print(psi.pretty())
psi.qinit = '\A \E'
psi.moore = False
psi.plus_one = False
#ctrl=synth.synthesize(psi, ignore_sys_init=False, solver='gr1c')
strategy = omega_int.synthesize_enumerated_streett(psi)
use_cudd=False
spec=psi
aut = omega_int._grspec_to_automaton(spec)
sym.fill_blanks(aut)
bdd = omega_int._init_bdd(use_cudd)
aut.bdd = bdd
a = aut.build()
bdd.dump("file_a.pdf")
# what has been generated#
print(aut.init['env'])
print(aut.init['sys'])
print(aut.action['sys'])
print(bdd.false)
# what has been build from it
print(a.init['env'])
print(a.init['sys'])
print(a.action['sys'])
