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]
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))
self.chainer.add_rule(rules.InheritanceRule(self.chainer))
self.chainer.add_rule(rules.SimilarityRule(self.chainer))
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, 5):
self.chainer.add_rule(rule)
self.chainer.add_rule(rules.SubsetEvaluationRule(self.chainer))
self.chainer.add_rule(rules.InheritanceEvaluationRule(self.chainer))
self.chainer.add_rule(rules.ExtensionalSimilarityEvaluationRule(self.chainer))
self.chainer.add_rule(rules.EvaluationToMemberRule(self.chainer))
self.chainer.add_rule(rules.MemberToInheritanceRule(self.chainer))
self.chainer.add_rule(rules.MemberToSubsetRule(self.chainer))
self.chainer.add_rule(rules.AttractionRule(self.chainer))
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 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)
