def test_InversionRule(self):
rule = rules.InversionRule(self.chainer, types.InheritanceLink)
self._inh_animal_breathe()
result = self.chainer._apply_forward(rule)
print result
def create_chainer(self, atomspace):
self.chainer = Chainer(atomspace, stimulateAtoms=False, agent=self)
deduction_link_types = [types.InheritanceLink]
# types.SubsetLink, types.IntensionalInheritanceLink]
for link_type in deduction_link_types:
self.chainer.add_rule(rules.InversionRule(self.chainer, link_type))
self.chainer.add_rule(rules.DeductionRule(self.chainer, link_type))
def test_InversionRule_backward(self):
rule = rules.InversionRule(self.chainer, types.InheritanceLink)
self._inh_animal_breathe()
self.inh_breathe_animal = self.atomspace.add_link(types.InheritanceLink, [self.breathe, self.animal])
self.inh_breathe_animal.av = {'sti':1}
result = self.chainer._apply_backward(rule)
print result
def create_chainer(self, atomspace):
self.chainer = Chainer(atomspace,
stimulateAtoms=False,
agent=self,
learnRuleFrequencies=True)
# ImplicationLink is MixedImplicationLink, you could also have Extensional and Intensional Implication. etc. but that's a bit much.
# similarity_types = [types.SimilarityLink, types.ExtensionalSimilarityLink, types.IntensionalSimilarityLink]
# types.EquivalenceLink]
# conditional_probability_types = [types.InheritanceLink, types.SubsetLink, types.IntensionalInheritanceLink, types.ImplicationLink]
# always use the mixed inheritance types, because human inference is normally a mix of intensional and extensional
conditional_probability_types = [
types.InheritanceLink, types.ImplicationLink
]
similarity_types = [types.SimilarityLink, types.EquivalenceLink]
for link_type in conditional_probability_types:
self.chainer.add_rule(rules.InversionRule(self.chainer, link_type))
self.chainer.add_rule(rules.DeductionRule(self.chainer, link_type))
self.chainer.add_rule(
rules.ModusPonensRule(self.chainer, link_type))
# As a hack, use the standard DeductionRule for SimilarityLinks. It needs its own formula really.
for link_type in similarity_types:
self.chainer.add_rule(rules.DeductionRule(self.chainer, link_type))
# These two Rules create mixed links out of intensional and extensional links
self.chainer.add_rule(rules.InheritanceRule(self.chainer))
self.chainer.add_rule(rules.SimilarityRule(self.chainer))
# and/or/not
self.chainer.add_rule(rules.NotCreationRule(self.chainer))
self.chainer.add_rule(rules.NotEliminationRule(self.chainer))
for rule in rules.create_and_or_rules(self.chainer, 1, 2):
self.chainer.add_rule(rule)
# create probabilistic logical links out of MemberLinks
self.chainer.add_rule(rules.SubsetEvaluationRule(self.chainer))
self.chainer.add_rule(
rules.IntensionalInheritanceEvaluationRule(self.chainer))
self.chainer.add_rule(
rules.ExtensionalSimilarityEvaluationRule(self.chainer))
self.chainer.add_rule(
rules.IntensionalSimilarityEvaluationRule(self.chainer))
self.chainer.add_rule(rules.EvaluationToMemberRule(self.chainer))
self.chainer.add_rule(rules.MemberToInheritanceRule(self.chainer))
# AttractionLink could be useful for causality
self.chainer.add_rule(rules.AttractionRule(self.chainer))
for rule in temporal_rules.create_temporal_rules(self.chainer):
self.chainer.add_rule(rule)
def test_standardize_apart_input_output(self):
rule = rules.InversionRule(self.chainer, types.InheritanceLink)
(input, output) = rule.standardize_apart_input_output(self.chainer)
def create_chainer(self, atomspace):
self.chainer = Chainer(atomspace,
stimulateAtoms=False,
agent=self,
learnRuleFrequencies=False)
# ImplicationLink is MixedImplicationLink, you could also have Extensional and Intensional Implication. etc. but that's a bit much.
# similarity_types = [types.SimilarityLink, types.ExtensionalSimilarityLink, types.IntensionalSimilarityLink]
# types.EquivalenceLink]
# conditional_probability_types = [types.InheritanceLink, types.SubsetLink, types.IntensionalInheritanceLink, types.ImplicationLink]
# always use the mixed inheritance types, because human inference is normally a mix of intensional and extensional
conditional_probability_types = [
types.InheritanceLink, types.ImplicationLink
]
similarity_types = [types.SimilarityLink, types.EquivalenceLink]
for link_type in conditional_probability_types:
self.chainer.add_rule(rules.InversionRule(self.chainer, link_type))
self.chainer.add_rule(rules.DeductionRule(self.chainer, link_type))
self.chainer.add_rule(rules.InductionRule(self.chainer, link_type))
self.chainer.add_rule(rules.AbductionRule(self.chainer, link_type))
#self.chainer.add_rule(rules.ModusPonensRule(self.chainer, link_type))
# As a hack, use the standard DeductionRule for SimilarityLinks. It needs its own formula really.
for link_type in similarity_types:
self.chainer.add_rule(rules.DeductionRule(self.chainer, link_type))
# These two Rules create mixed links out of intensional and extensional links
self.chainer.add_rule(rules.InheritanceRule(self.chainer))
self.chainer.add_rule(rules.SimilarityRule(self.chainer))
# boolean links
for rule in boolean_rules.create_and_or_rules(self.chainer, 1, 2):
self.chainer.add_rule(rule)
# create probabilistic logical links out of MemberLinks
self.chainer.add_rule(rules.AndEvaluationRule(self.chainer))
self.chainer.add_rule(rules.OrEvaluationRule(self.chainer))
# These two "macro rules" make the individual rules redundant
self.chainer.add_rule(rules.ExtensionalLinkEvaluationRule(
self.chainer))
self.chainer.add_rule(rules.IntensionalLinkEvaluationRule(
self.chainer))
#self.chainer.add_rule(rules.SubsetEvaluationRule(self.chainer))
self.chainer.add_rule(rules.NegatedSubsetEvaluationRule(self.chainer))
#self.chainer.add_rule(rules.ExtensionalSimilarityEvaluationRule(self.chainer))
#self.chainer.add_rule(rules.IntensionalInheritanceEvaluationRule(self.chainer))
#self.chainer.add_rule(rules.IntensionalSimilarityEvaluationRule(self.chainer))
self.member_rules = [rules.EvaluationToMemberRule(self.chainer)]
self.member_rules += rules.create_general_evaluation_to_member_rules(
self.chainer)
for rule in self.member_rules:
self.chainer.add_rule(rule)
# It's important to have both of these
self.chainer.add_rule(rules.MemberToInheritanceRule(self.chainer))
# self.chainer.add_rule(rules.InheritanceToMemberRule(self.chainer))
# AttractionLink could be useful for causality
self.chainer.add_rule(rules.AttractionRule(self.chainer))
def create_chainer(self, atomspace):
# Note: using stimulateAtoms will cause a segfault if you create the Agent from the Python shell (use the agents-start command in the cogserver shell). It's because giving atoms stimulus only works if the MindAgent is added to the CogServer's list of agents.
self.chainer = Chainer(atomspace,
stimulateAtoms=False,
agent=self,
learnRuleFrequencies=True)
# ImplicationLink is MixedImplicationLink, you could also have Extensional and Intensional Implication. etc. but that's a bit much.
# similarity_types = [types.SimilarityLink, types.ExtensionalSimilarityLink, types.IntensionalSimilarityLink]
# types.EquivalenceLink]
# conditional_probability_types = [types.InheritanceLink, types.SubsetLink, types.IntensionalInheritanceLink, types.ImplicationLink]
# always use the mixed inheritance types, because human inference is normally a mix of intensional and extensional
conditional_probability_types = [
types.InheritanceLink, types.ImplicationLink,
types.PredictiveImplicationLink
]
similarity_types = [types.SimilarityLink, types.EquivalenceLink]
for link_type in conditional_probability_types:
self.chainer.add_rule(rules.InversionRule(self.chainer, link_type))
self.chainer.add_rule(rules.DeductionRule(self.chainer, link_type))
self.chainer.add_rule(rules.InductionRule(self.chainer, link_type))
self.chainer.add_rule(rules.AbductionRule(self.chainer, link_type))
# Seems better than Modus Ponens - it doesn't make anything up
self.chainer.add_rule(
rules.TermProbabilityRule(self.chainer, link_type))
self.chainer.add_rule(
rules.ModusPonensRule(self.chainer, link_type))
for link_type in similarity_types:
# SimilarityLinks don't require an InversionRule obviously
self.chainer.add_rule(
rules.TransitiveSimilarityRule(self.chainer, link_type))
self.chainer.add_rule(
rules.SymmetricModusPonensRule(self.chainer, link_type))
self.chainer.add_rule(
predicate_rules.EvaluationImplicationRule(self.chainer))
# These two Rules create mixed links out of intensional and extensional links
self.chainer.add_rule(rules.InheritanceRule(self.chainer))
self.chainer.add_rule(rules.SimilarityRule(self.chainer))
# boolean links
for rule in boolean_rules.create_and_or_rules(self.chainer, 2, 8):
self.chainer.add_rule(rule)
# create probabilistic logical links out of MemberLinks
self.chainer.add_rule(rules.AndEvaluationRule(self.chainer))
self.chainer.add_rule(rules.OrEvaluationRule(self.chainer))
# These two "macro rules" make the individual rules redundant
self.chainer.add_rule(rules.ExtensionalLinkEvaluationRule(
self.chainer))
self.chainer.add_rule(rules.IntensionalLinkEvaluationRule(
self.chainer))
#self.chainer.add_rule(rules.SubsetEvaluationRule(self.chainer))
self.chainer.add_rule(rules.NegatedSubsetEvaluationRule(self.chainer))
#self.chainer.add_rule(rules.ExtensionalSimilarityEvaluationRule(self.chainer))
#self.chainer.add_rule(rules.IntensionalInheritanceEvaluationRule(self.chainer))
#self.chainer.add_rule(rules.IntensionalSimilarityEvaluationRule(self.chainer))
self.member_rules = [
rules.EvaluationToMemberRule(self.chainer),
rules.MemberToEvaluationRule(self.chainer)
]
self.member_rules += rules.create_general_evaluation_to_member_rules(
self.chainer)
for rule in self.member_rules:
self.chainer.add_rule(rule)
# It's important to have both of these
self.chainer.add_rule(rules.MemberToInheritanceRule(self.chainer))
# self.chainer.add_rule(rules.InheritanceToMemberRule(self.chainer))
# AttractionLink could be useful for causality
self.chainer.add_rule(rules.AttractionRule(self.chainer))
self.chainer.add_rule(quantifier_rules.ScholemRule(self.chainer))