def p_literal(p):
'''literal : LPAREN NOT_KEY predicate RPAREN
| predicate'''
if len(p) == 2:
p[0] = Literal.positive(p[1])
elif len(p) == 5:
p[0] = Literal.negative(p[3])
def main():
numQueries = 0
queries = []
numTruths = 0
truths = []
with open("./input.txt") as inp:
line = inp.readline()
# First line is always the number of queries
numQueries = int(line)
# n queries
for i in range(numQueries):
line = inp.readline()
queries.append(line.strip())
line = inp.readline()
# This is followed by the number of truths
numTruths = int(line)
for i in range(numTruths):
line = inp.readline()
truths.append(line.strip())
kb = KnowledgeBase(truths)
results = []
for q in queries:
lit = Literal(q, len(truths) + 1)
lit.negate()
result = kb.proveByResolution2([lit], 0)
print("{}: {}".format(q, result))
results.append(str(result).upper())
with open('./output.txt', 'w') as op:
op.write('\n'.join([r for r in results]))
def compositeToBasicRules(self, pred, body, varOrder = None):
variables = set()
rules = []
rule = Rule()
passOperator = ''
for literal in body:
newLiteral = None
if isinstance(literal, string_types):
for literalString in literal.split('^'):
if re.match('[!#]?[a-z]+[_0-9\([a-zA-Z,]+\)]?' , literalString):
newLiteral = Literal.fromString(literalString)
rule.body.append(newLiteral)
variables = variables.union(newLiteral.vars())
elif re.match('[!#]', literalString):
passOperator = literalString
else:
freshPred = freshPredicate()
(newLiteralVars, newLiteralRules) = Rule.compositeToBasicRules(freshPred, literal)
rules += newLiteralRules
newLiteral = Literal.fromString( passOperator + freshPred + '(' + ','.join(newLiteralVars) + ')')
rule.body.append(newLiteral)
variables = variables.union(newLiteral.vars())
passOperator = ''
if varOrder is not None:
rule.head = Atom.fromString(pred + '(' + varOrder + ')')
else:
rule.head = Atom.fromString(pred + '(' + ','.join(map(str, variables)) + ')')
rules.append(rule)
return (variables, rules)
def __init__(self, atoms):
self._atoms = atoms
self._terms = []
self._weights = {}
self.__index = {}
for atom in self._atoms:
self.__index[Literal.positive(atom)] = set()
self.__index[Literal.negative(atom)] = set()
def p_literal(p):
'''literal : NEGATION LPAREN atom RPAREN
| NEGATION atom
| atom'''
if len(p) == 5:
p[0] = Literal.negative(p[3])
elif len(p) == 3:
p[0] = Literal.negative(p[2])
else:
p[0] = Literal.positive(p[1])
def add_to_domain(domain):
############################################
# Let's now create TYPES AND PREDICATES
############################################
domain.add_type('boxes')
domain.add_pred('open', [('?x', 'boxes'), ('?z', 'block')])
domain.del_pred('handempty', 0)
# print(repr(domain.operators[3]))
############################################
# Let's now create an OPERATOR
############################################
# print(domain.operators[3].precond)
params = [Term.variable('?x', type='blocks'), Term.variable('?y', type='blocks')]
precond=[]
precond.append(Literal(Predicate('=', [Term.variable('?x'), Term.variable('?y')]), False))
precond.append(Literal(Predicate('on', [Term.variable('?x'), Term.variable('?y')]), True))
precond.append(Literal(Predicate('clear', [Term.variable('?x')]), True))
precond.append(Literal(Predicate('handempty', []), True))
# print(domain.operators[3].effects)
effect=[]
effect.append((1.0, Literal(Predicate('holding', [Term.variable('?x')]), True)))
effect.append((1.0, Literal(Predicate('clear', [Term.variable('?y')]), True)))
effect.append((1.0, Literal(Predicate('clear', [Term.variable('?x')]), False)))
effect.append((1.0, Literal(Predicate('handempty', []), False)))
effect.append((1.0, Literal(Predicate('on', [Term.variable('?x'), Term.variable('?y')]), False)))
# print(repr(effect))
domain.add_action('pick-up-b', params, precond, effect)
def __update_index(self, term, index):
term = set(term)
for atom in self._atoms:
positive_literal = Literal.positive(atom)
negative_literal = Literal.negative(atom)
if positive_literal in term:
self.__index[positive_literal].add(index)
continue
if negative_literal in term:
self.__index[negative_literal].add(index)
continue
self.__index[positive_literal].add(index)
self.__index[negative_literal].add(index)
def get_elements_from_string(self, text):
formula_strings = text.split(" ")
elements = []
for formula_string in formula_strings:
if formula_string == '' or formula_string == text:
continue
if utils.is_digit(formula_string):
if (int(formula_string) == 0):
break
literal = Literal(formula_string)
elements.append(literal)
else:
inner_elements = self.get_elements_from_string(formula_string)
# if we have more than one inner element, wrap them in another formula
if len(inner_elements) > 0:
new_formula = Formula(Operation.OR)
new_formula.add_elements(inner_elements)
elements.append(new_formula)
else:
elements.extend(inner_elements)
return elements
def start(self, description=None, **options):
super(SCryptoMinisatEngine, self).start(description, **options)
self.start_time = time.time()
if not self.cnf:
vars = sorted(self.description.get_variables(),
key=lambda x: not x.name.startswith("AB"))
self.vars = TwoWayDict(
dict((i, v.name) for i, v in enumerate(vars)))
self.ab_vars = [
i for i, n in self.vars.iteritems() if n.startswith("AB")
]
# add clauses for the input/output variables which are constrained
self.core_map = {}
self.clauses = []
for s in self.description.get_sentences():
self.clauses.extend(self.sentence_interpreters[type(s)](
self, s, self.vars))
for v in self.description.get_variables():
if v.value is not None:
self.clauses.append(Clause([Literal(v.name, v.value)]))
for c in self.clauses:
self.cnf += "\n" + to_dimacs_clause(c, self.vars)
self.stats['cnf_clauses'] = len(self.clauses)
self.max_out = max(self.ab_vars) + 1
return self.solve()
def start(self, description=None, **options):
super(PicoSatEngine, self).start(description, **options)
if not self.cnf:
self.vars = TwoWayDict(
dict((i, v.name)
for i, v in enumerate(self.description.get_variables())))
# add clauses for the input/output variables which are constrained
sentences = self.description.get_sentences()
for v in self.description.get_variables():
if v.value is not None:
sentences.append(Clause([Literal(v.name, v.value)]))
self.core_map = {}
self.clauses = self.interpret_sentences(sentences)
self.cnf = "p cnf %d %d" % (max(self.vars.keys()) + 1,
len(self.clauses))
for c in self.clauses:
self.cnf += "\n" + to_dimacs_clause(c, self.vars)
# print self.cnf
p = PicoSatWrapper()
p.start(self.cnf, calculate_unsat_core=True)
# print "\n".join(outlines)
self.result = self.parse_output(p.outlines, self.vars,
p.get_core_file_name())
return self.result
def __init__(self, lit_list, model, _id):
self.literals = [Literal(model.get_var_obj(abs(lit_num)), lit_num > 0) for lit_num in lit_list]
self._length = len(lit_list)
self.id = _id
if( self._length >= 2):
self._watched_lit = self.literals[:2]
else:
self._watched_lit = [self.literals[0]]
def parse_clause(clause_str):
list_literal_str = clause_str.strip().split('OR')
clause = Clause()
for literal_str in list_literal_str:
literal = Literal.parse_literal(literal_str)
clause.add(literal)
clause.flatten()
return clause
def test_combine(self):
parser = Parser()
transition_model = '''
1.000::running(c1, 1) :- running(c1, 0).
'''
atoms1, rules1 = parser.parse(transition_model)
wdnf1 = WeightedDNF(set(atoms1.keys()))
for index, rule in enumerate(rules1):
pos_term = tuple(
sorted([Literal.positive(rule.head)] + list(rule.body)))
wdnf1.add_term(pos_term, rule.probability)
neg_term = tuple(
sorted([Literal.negative(rule.head)] + list(rule.body)))
wdnf1.add_term(neg_term, 1 - rule.probability)
value_model = '''
V(0) :- not(running(c1, 1)), not(running(c2, 1)), not(running(c3, 1)).
V(1) :- running(c1, 1), not(running(c2, 1)), not(running(c3, 1)).
V(1) :- not(running(c1, 1)), running(c2, 1), not(running(c3, 1)).
V(1) :- not(running(c1, 1)), not(running(c2, 1)), running(c3, 1).
V(2) :- running(c1, 1), running(c2, 1), not(running(c3, 1)).
V(2) :- running(c1, 1), not(running(c2, 1)), running(c3, 1).
V(2) :- not(running(c1, 1)), running(c2, 1), running(c3, 1).
V(3) :- running(c1, 1), running(c2, 1), running(c3, 1).
'''
atoms2, rules2 = parser.parse(value_model)
wdnf2 = WeightedDNF(set(atoms2.keys()))
for index, rule in enumerate(rules2):
wdnf2.add_term(sorted(rule.body), float(rule.head[2:-1]))
prod = combine(wdnf1, wdnf2, float.__mul__)
self.assertEqual(len(prod), len(wdnf2))
pos = Literal.positive('running(c1,1)')
neg = Literal.negative('running(c1,1)')
r1 = Literal.positive('running(c1,0)')
for weight, term in prod:
self.assertEqual(len(term), 4)
if neg in term:
self.assertEqual(weight, 0.0)
if pos in term:
count = sum([
literal.is_positive() for literal in term if literal != r1
])
self.assertEqual(weight, float(count))
def fromString(self, string):
newRule = Rule()
string = string.replace(' ','')
tmp = string.split(':-')
newRule.head = Atom.fromString(tmp[0])
if len(tmp) > 1:
for literalString in tmp[1].split('^'):
newRule.body.append(Literal.fromString(literalString))
return newRule
def getProofByResolution(self, l: Literal):
l.negate() # Negate the literal!
inputLiterals = [l]
literalCopy = deepcopy(self.literalMap)
sentenceCopy = deepcopy(self.sentences)
alreadySelected = {}
while True:
# Iterate though all input literals to get the best matching sentence in KB
sentenceCountMap = {}
bestSentence = -1
for inlit in inputLiterals:
key = (inlit.identifier, inlit.litType)
locations = literalCopy.get(key, [])
for loc in locations:
sentence = sentenceCopy[loc]
literals = sentence.literals
for lit in literals:
if (lit.identifier, lit.litType) == key and (
lit.canBeResolvedBy(inlit)
or inlit.canBeResolvedBy(lit)
) and lit.negated != inlit.negated:
# These can be resolved
sentenceCountMap[loc] = sentenceCountMap.get(
loc, 0) + 1
if sentenceCountMap[loc] > sentenceCountMap.get(
bestSentence, -1):
bestSentence = loc
break
# There is no sentence with at least one of the appropriate literals
if bestSentence == -1:
return False
neededSentence = sentenceCopy[bestSentence]
inputLiterals = getResolution(neededSentence, inputLiterals)
if len(inputLiterals) == 0:
self.sentences.append(Sentence(str(l)))
self.literalMap[(l.identifier,
l.litType)] = self.literalMap.get(
(l.identifier, l.litType),
[]) + [len(self.sentences) - 1]
return True
def substitute(lit: Literal, unification: dict):
# print("Trying to unify {} with {}".format(str(lit), unification)) # For debug purposes
if lit.litType != 'P':
# If the unification does not contain this identifier, then just return this
lit = unification.get(lit, lit)
return lit
for i in range(len(lit.args)):
lit.args[i] = substitute(lit.args[i], unification)
return lit
def create_yices_code(self):
code = []
for v in self.description.get_variables():
self.vars[v.name] = self.next_var
self.next_var += 1
if v.value is not None:
code.append(
self.to_dimacs_clause(
Clause([Literal(v.name, v.value)],
weight=self.MAX_WEIGHT + 1), self.vars))
for s in self.description.get_sentences():
for c in self.sentence_interpreters[type(s)](self, s, None):
code.append(self.to_dimacs_clause(c, self.vars))
return len(code), "\n".join(code)
def setUp(cls):
cls.r1 = 'running(c1,1)'
cls.r2 = 'running(c2,1)'
cls.r3 = 'running(c3,1)'
cls.lp1 = Literal.positive(cls.r1)
cls.ln1 = Literal.negative(cls.r1)
cls.lp2 = Literal.positive(cls.r2)
cls.ln2 = Literal.negative(cls.r2)
cls.lp3 = Literal.positive(cls.r3)
cls.ln3 = Literal.negative(cls.r3)
def __init__(self, inp : str, sNum : int):
# Replace implication with #
sent = inp.replace("=>", "#").strip()
isImplication = sent.rfind("#") != -1
sent = sent.split(" ")
# Alternate to get literals
self.literals = [Literal(l, sNum) for l in sent[::2]]
# Alternate with one offset to get connectors
self.connectors = [l for l in sent[1::2]]
# If implication, convert to disjunction
if isImplication:
self.connectors[-1] = "|"
for i in range(len(self.connectors) - 1):
self.connectors[i] = "&" if self.connectors[i] == "|" else "|"
for i in range(len(self.literals) - 1):
self.literals[i].negate()
def start(self, description=None, **options):
super(YicesCnEngine, self).start(description, **options)
if not self.cnf:
vars = sorted(self.description.get_variables(),
key=lambda x: not x.name.startswith("AB"))
self.vars = TwoWayDict(
dict((i, v.name) for i, v in enumerate(vars)))
self.ab_vars = [
i for i, n in self.vars.iteritems() if n.startswith("AB")
]
sentences = self.description.get_sentences()
for v in self.description.get_variables():
if v.value is not None:
sentences.append(Clause([Literal(v.name, v.value)]))
self.core_map = {}
self.clauses = self.interpret_sentences(sentences)
for c in self.clauses:
self.cnf += "\n" + self.to_dimacs_clause(c, self.vars)
self.stats['cnf_clauses'] = len(self.clauses)
self.construct_cardinality_network(**options)
self.stats['num_rules'] = self.stats.get(
'cnf_clauses', 0) + self.stats.get('net_clauses', 0) + 1
self.stats['num_vars'] = max(self.cn.vars)
# cnf = "p cnf %d %d\n" % (self.stats['num_vars'], self.stats['num_rules']) + self.cnf + self.cnf_cn
cnf = self.cnf + self.cnf_cn
cnf += "\n\n-%d 0\n" % self.cn.vars[options.get('max_card', 1)]
p = SimpleYicesWrapper()
p.start(
SimpleDescription(cnf, self.stats['num_vars'],
self.stats['num_rules']))
self.result = self.parse_output(p.outlines, self.vars, self.ab_vars)
return self.result
def __lt__(self, value):
sql = '%s < %%s' % self
return Literal(sql, self, value)
def test_add_term(self):
# value function
wdnf1 = wdnf.WeightedDNF(self.atoms1)
self.assertEqual(len(wdnf1), 0)
for index, rule in enumerate(self.rules1):
wdnf1.add_term(rule.body, int(rule.head[2:-1]))
self.assertEqual(len(wdnf1), index + 1)
fluents = ['running(c1,1)', 'running(c2,1)', 'running(c3,1)']
for fluent in fluents:
pos = Literal.positive(fluent)
neg = Literal.negative(fluent)
t1 = set(wdnf1.terms_by_literals([pos]))
t2 = set(wdnf1.terms_by_literals([neg]))
self.assertEqual(len(t1), len(wdnf1) / 2)
self.assertEqual(len(t2), len(wdnf1) / 2)
self.assertEqual(t1 & t2, set())
self.assertEqual(t1 | t2, set(wdnf1.terms))
values = [0, 1, 2, 3]
terms_sets = [set(wdnf1.terms_by_weight(v)) for v in values]
self.assertEqual(sum([len(s) for s in terms_sets]), len(wdnf1))
for i in range(len(terms_sets[-1])):
for j in range(i + 1, len(terms_sets)):
self.assertEqual(terms_sets[i] & terms_sets[j], set())
# transition function
wdnf2 = wdnf.WeightedDNF(self.atoms2)
self.assertEqual(len(wdnf2), 0)
for index, rule in enumerate(self.rules2):
pos_term = tuple([Literal.positive(rule.head)] + list(rule.body))
wdnf2.add_term(pos_term, rule.probability)
neg_term = tuple([Literal.negative(rule.head)] + list(rule.body))
wdnf2.add_term(neg_term, 1 - rule.probability)
self.assertEqual(len(wdnf2), 2 * len(self.rules2))
next_state_fluents = [
'running(c1,1)', 'running(c2,1)', 'running(c3,1)'
]
for fluent in next_state_fluents:
pos = Literal.positive(fluent)
neg = Literal.negative(fluent)
t1 = set(wdnf2.terms_by_literals([pos]))
t2 = set(wdnf2.terms_by_literals([neg]))
self.assertEqual(len(t1), len(t2))
inter = set(term for _, term in t1 & t2)
self.assertTrue(pos not in inter)
self.assertTrue(neg not in inter)
actions = ['reboot(c1)', 'reboot(c2)', 'reboot(c3)']
for action in actions:
pos = Literal.positive(action)
neg = Literal.negative(action)
t1 = set(wdnf2.terms_by_literals([pos]))
t2 = set(wdnf2.terms_by_literals([neg]))
inter = set(term for _, term in t1 & t2)
self.assertTrue(pos not in inter)
self.assertTrue(neg not in inter)
if action == actions[0]:
self.assertEqual(len(t1), len(wdnf2) - 6)
self.assertEqual(len(t2), len(wdnf2) - 2)
if action == actions[2]:
self.assertEqual(len(t1), len(wdnf2) - 6)
self.assertEqual(len(t2), len(wdnf2) - 2)
def p_ATOMIC_SENTENCE(t):
'''ATOMIC_SENTENCE : STRING LPAREN STRING RPAREN'''
t[0] = Literal(t[1], t[3], "ATOMIC_SENTENCE")
def build_formula():
b1 = Bool("b1")
b2 = Bool("b2")
b3 = Bool("b3")
b4 = Bool("b4")
b5 = Bool("b5")
b6 = Bool("b6")
b7 = Bool("b7")
b8 = Bool("b8")
b9 = Bool("b9")
b10 = Bool("b10")
b11 = Bool("b11")
b12 = Bool("b12")
b13 = Bool("b13")
b14 = Bool("b14")
b15 = Bool("b15")
b16 = Bool("b16")
lit_b1 = Literal(b1, negated=False)
lit_b2 = Literal(b2, negated=False)
lit_b3 = Literal(b3, negated=False)
lit_b4 = Literal(b4, negated=False)
lit_b5 = Literal(b5, negated=False)
lit_b6 = Literal(b6, negated=False)
lit_b7 = Literal(b7, negated=False)
lit_b8 = Literal(b8, negated=False)
lit_b9 = Literal(b9, negated=False)
lit_b10 = Literal(b10, negated=False)
lit_b11 = Literal(b11, negated=False)
lit_b12 = Literal(b12, negated=False)
lit_b13 = Literal(b13, negated=False)
lit_b14 = Literal(b14, negated=False)
lit_b15 = Literal(b15, negated=False)
lit_b16 = Literal(b16, negated=False)
lit_b16_bar = Literal(b16, negated=True)
lit_b15_bar = Literal(b15, negated=True)
lit_b14_bar = Literal(b14, negated=True)
lit_b11_bar = Literal(b11, negated=True)
lit_b10_bar = Literal(b10, negated=True)
lit_b9_bar = Literal(b9, negated=True)
lit_b8_bar = Literal(b8, negated=True)
lit_b7_bar = Literal(b7, negated=True)
lit_b6_bar = Literal(b6, negated=True)
lit_b5_bar = Literal(b5, negated=True)
lit_b4_bar = Literal(b4, negated=True)
lit_b2_bar = Literal(b2, negated=True)
clause0 = Clause([lit_b1, lit_b2])
clause1 = Clause([lit_b2_bar, lit_b4_bar])
clause2 = Clause([lit_b3, lit_b4])
clause3 = Clause([lit_b4_bar, lit_b5_bar])
clause4 = Clause([lit_b5, lit_b6_bar])
clause5 = Clause([lit_b6, lit_b7_bar])
clause6 = Clause([lit_b6, lit_b7])
clause7 = Clause([lit_b7, lit_b16_bar])
clause8 = Clause([lit_b8, lit_b9_bar])
clause9 = Clause([lit_b8_bar, lit_b14_bar])
clause10 = Clause([lit_b9, lit_b10])
clause11 = Clause([lit_b9, lit_b10_bar])
clause12 = Clause([lit_b10_bar, lit_b11_bar])
clause13 = Clause([lit_b10, lit_b12])
clause14 = Clause([lit_b11, lit_b12])
clause15 = Clause([lit_b13, lit_b14])
clause16 = Clause([lit_b14, lit_b15_bar])
clause17 = Clause([lit_b15, lit_b16])
clause_list = [
clause0, clause1, clause2, clause3, clause4, clause5, clause6, clause7,
clause8, clause9, clause10, clause11, clause12, clause13, clause14,
clause15, clause16, clause17
]
atom_list = [
b1, b2, b3, b4, b5, b6, b7, b8, b9, b10, b11, b12, b13, b14, b15, b16
]
return clause_list, atom_list
def __eq__(self, value):
sql = '%s = %%s' % self
return Literal(sql, self, value)
def __ne__(self, value):
sql = '%s <> %%s' % self
return Literal(sql, self, value)
def __gt__(self, value):
sql = '%s > %%s' % self
return Literal(sql, self, value)
def add_game(self, game):
for game_position in game:
literal = Literal(game_position)
self.add_element(literal)
def in_(self, value):
if type(value) == list or type(value) == tuple:
sql = '%s IN (%s)' % (self, ', '.join(['%s' for item in value]))
else:
sql = '%s IN (%%s)' % self
return Literal(sql, self, value)
def like(self, value):
sql = '%s LIKE %%s' % self
return Literal(sql, self, str(value))
