def check_consistency(self, assumptions, verbose=False):
# Set up two thread, Prover and ModelBuilder to run in parallel
prover = inference.get_prover(assumptions=assumptions)
model_builder = inference.get_model_builder(assumptions=assumptions)
prover_result = [None]
prover_thread = ProverThread(prover, prover_result, verbose)
model_builder_result = [None]
model_builder_thread = ModelBuilderThread(model_builder, model_builder_result, verbose)
prover_thread.start()
model_builder_thread.start()
while not prover_result[0] and not model_builder_result[0]:
# wait until either the prover or the model builder is done
pass
if prover_result[0]:
consistency = prover_result[0]
else:
consistency = model_builder_result[0]
return (consistency == 'consistent')
def demo_inference_tagger(verbose=False):
tagger = RTEInferenceTagger()
text = 'John see a car'
print 'Text: ', text
hyp = 'John watch an auto'
print 'Hyp: ', hyp
# text_ex = logic.LogicParser().parse('some e x y.((david x) and ((own e) and ((subj e x) and ((obj e y) and (car y)))))))')
# hyp_ex = logic.LogicParser().parse('some e x y.((david x) and ((have e) and ((subj e x) and ((obj e y) and (auto y)))))))')
text_drs_list = drt_glue.parse_to_meaning(text, dependency=True, verbose=verbose)
if text_drs_list:
text_ex = text_drs_list[0].simplify().toFol()
else:
print 'ERROR: No readings were be generated for the Text'
hyp_drs_list = drt_glue.parse_to_meaning(hyp, dependency=True, verbose=verbose)
if hyp_drs_list:
hyp_ex = hyp_drs_list[0].simplify().toFol()
else:
print 'ERROR: No readings were be generated for the Hypothesis'
print 'Text: ', text_ex
print 'Hyp: ', hyp_ex
print ''
#1. proof T -> H
#2. proof (BK & T) -> H
#3. proof :(BK & T)
#4. proof :(BK & T & H)
#5. satisfy BK & T
#6. satisfy BK & T & H
result = inference.get_prover(hyp_ex, [text_ex]).prove()
print 'prove: T -> H: %s' % result
if result:
print 'Logical entailment\n'
else:
print 'No logical entailment\n'
bk = tagger._generate_BK(text, hyp, verbose)
bk_exs = [bk_pair[0] for bk_pair in bk]
print 'Generated Background Knowledge:'
for bk_ex in bk_exs:
print bk_ex.infixify()
print ''
result = inference.get_prover(hyp_ex, [text_ex]+bk_exs).prove()
print 'prove: (T & BK) -> H: %s' % result
if result:
print 'Logical entailment\n'
else:
print 'No logical entailment\n'
# Check if the background knowledge axioms are inconsistant
result = inference.get_prover(assumptions=bk_exs+[text_ex]).prove()
print 'prove: (BK & T): %s' % result
if result:
print 'Inconsistency -> Entailment unknown\n'
else:
print 'No inconsistency\n'
result = inference.get_prover(assumptions=bk_exs+[text_ex, hyp_ex]).prove()
print 'prove: (BK & T & H): %s' % result
if result:
print 'Inconsistency -> Entailment unknown\n'
else:
print 'No inconsistency\n'
result = inference.get_model_builder(assumptions=bk_exs+[text_ex]).build_model()
print 'satisfy: (BK & T): %s' % result
if result:
print 'No inconsistency\n'
else:
print 'Inconsistency -> Entailment unknown\n'
result = inference.get_model_builder(assumptions=bk_exs+[text_ex, hyp_ex]).build_model()
print 'satisfy: (BK & T & H): %s' % result
if result:
print 'No inconsistency\n'
else:
print 'Inconsistency -> Entailment unknown\n'
def _parallel_prove_satisfy(self, goal=None, assumptions=[]):
prover = inference.get_prover(assumptions=bk_exs+[text_ex])
model_builder = inference.get_model_builder(assumptions=bk_exs+[text_ex])
inconsistent = ProverThread(prover).start()
consistent = ModelBuilderThread(model_builder).start()