class AllRules(object):
def __init__(self, atomspace, chainer):
# contains every rule
self.chainer = Chainer(atomspace,
stimulateAtoms=False,
agent=self,
learnRuleFrequencies=False)
# contains only some rules
self.test_chainer = chainer
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(InversionRule(self.chainer, link_type)) # Rule test exists.
self.chainer.add_rule(DeductionRule(self.chainer, link_type)) # Rule test exists.
self.chainer.add_rule(InductionRule(self.chainer, link_type)) # Rule test exists.
self.chainer.add_rule(AbductionRule(self.chainer, link_type)) # Rule test exists.
# Seems better than Modus Ponens - it doesn't make anything up
self.chainer.add_rule(TermProbabilityRule(self.chainer, link_type))
self.chainer.add_rule(ModusPonensRule(self.chainer, link_type))
self.chainer.add_rule(PreciseModusPonensRule(self.chainer, link_type))
for link_type in similarity_types:
# SimilarityLinks don't require an InversionRule obviously
self.chainer.add_rule(
TransitiveSimilarityRule(self.chainer, link_type)) # Rule test exists.
self.chainer.add_rule(
SymmetricModusPonensRule(self.chainer, link_type))
self.chainer.add_rule(EvaluationImplicationRule(self.chainer)) # No test, appears to produce a TV not new nodes.
# These two Rules create mixed links out of intensional and
# extensional links
self.chainer.add_rule(InheritanceRule(self.chainer)) # Rule test exists.
self.chainer.add_rule(SimilarityRule(self.chainer))
for link_type in conditional_probability_types:
self.chainer.add_rule(AndToSubsetRule1(self.chainer, link_type))
for N in xrange(2, 8):
self.chainer.add_rule(AndToSubsetRuleN(self.chainer, link_type, N))
# boolean links
for rule in create_and_or_rules(self.chainer, 2, 8):
self.chainer.add_rule(rule)
for N in xrange(2, 8):
self.chainer.add_rule(
boolean_rules.AndBulkEvaluationRule(self.chainer, N))
for N in xrange(3, 8):
self.chainer.add_rule(
boolean_rules.NegatedAndBulkEvaluationRule(self.chainer, N))
general_evaluation_to_member_rules = create_general_evaluation_to_member_rules(self.chainer)
for rule in general_evaluation_to_member_rules:
self.chainer.add_rule(rule)
# create probabilistic logical links out of MemberLinks
self.chainer.add_rule(AndEvaluationRule(self.chainer))
self.chainer.add_rule(OrEvaluationRule(self.chainer))
self.chainer.add_rule(ExtensionalLinkEvaluationRule(self.chainer))
self.chainer.add_rule(IntensionalLinkEvaluationRule(self.chainer))
self.chainer.add_rule(SubsetEvaluationRule(self.chainer))
self.chainer.add_rule(NegatedSubsetEvaluationRule(self.chainer))
self.chainer.add_rule(
ExtensionalSimilarityEvaluationRule(self.chainer))
self.chainer.add_rule(
IntensionalInheritanceEvaluationRule(self.chainer))
self.chainer.add_rule(
IntensionalSimilarityEvaluationRule(self.chainer))
# self.member_rules = [GeneralEvaluationToMemberRule(self.chainer),
# MemberToEvaluationRule(self.chainer)]
# self.member_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(MemberToInheritanceRule(self.chainer))
# self.chainer.add_rule(InheritanceToMemberRule(self.chainer))
# AttractionLink could be useful for causality
self.chainer.add_rule(AttractionRule(self.chainer))
self.chainer.add_rule(ScholemRule(self.chainer))
for rule in create_temporal_rules(self.chainer):
self.chainer.add_rule(rule)
boolean_transformation_rules = create_boolean_transformation_rules(self.chainer)
for rule in boolean_transformation_rules:
self.chainer.add_rule(rule)
def lookup_rule(self, rule_schema_node):
rule = self.chainer.lookup_rule(rule_schema_node.name)
rule._chainer = self.test_chainer
return rule
class AllRules(object):
def __init__(self, atomspace, chainer):
from pln.rules import rules, temporal_rules, boolean_rules, quantifier_rules, context_rules, predicate_rules
# contains every rule
self.chainer = Chainer(atomspace, stimulateAtoms = False, agent = self, learnRuleFrequencies=False)
# contains only some rules
self.test_chainer = chainer
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))
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))
for rule in temporal_rules.create_temporal_rules(self.chainer):
self.chainer.add_rule(rule)
def lookup_rule(self, rule_schema_node):
rule = self.chainer.lookup_rule(rule_schema_node.name)
rule._chainer = self.test_chainer
return rule
class AllRules(object):
def __init__(self, atomspace, chainer):
# contains every rule
self.chainer = Chainer(atomspace,
stimulateAtoms=False,
agent=self,
learnRuleFrequencies=False)
# contains only some rules
self.test_chainer = chainer
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(InversionRule(
self.chainer, link_type)) # Rule test exists.
self.chainer.add_rule(DeductionRule(
self.chainer, link_type)) # Rule test exists.
self.chainer.add_rule(InductionRule(
self.chainer, link_type)) # Rule test exists.
self.chainer.add_rule(AbductionRule(
self.chainer, link_type)) # Rule test exists.
# Seems better than Modus Ponens - it doesn't make anything up
self.chainer.add_rule(TermProbabilityRule(self.chainer, link_type))
self.chainer.add_rule(ModusPonensRule(self.chainer, link_type))
self.chainer.add_rule(
PreciseModusPonensRule(self.chainer, link_type))
self.chainer.add_rule(
AndAs1stArgInsideLinkRule(self.chainer, link_type))
self.chainer.add_rule(
AndAs2ndArgInsideLinkRule(self.chainer, link_type))
for link_type in similarity_types:
# SimilarityLinks don't require an InversionRule obviously
self.chainer.add_rule(
TransitiveSimilarityRule(self.chainer,
link_type)) # Rule test exists.
self.chainer.add_rule(
SymmetricModusPonensRule(self.chainer, link_type))
self.chainer.add_rule(EvaluationImplicationRule(
self.chainer)) # No test, appears to produce a TV not new nodes.
# These two Rules create mixed links out of intensional and
# extensional links
self.chainer.add_rule(InheritanceRule(
self.chainer)) # Rule test exists.
self.chainer.add_rule(SimilarityRule(self.chainer))
for link_type in conditional_probability_types:
self.chainer.add_rule(AndToSubsetRule1(self.chainer, link_type))
for N in xrange(2, 8):
self.chainer.add_rule(
AndToSubsetRuleN(self.chainer, link_type, N))
# boolean links
for rule in create_and_or_rules(self.chainer, 2, 8):
self.chainer.add_rule(rule)
for N in xrange(2, 8):
self.chainer.add_rule(
boolean_rules.AndBulkEvaluationRule(self.chainer, N))
for N in xrange(3, 8):
self.chainer.add_rule(
boolean_rules.NegatedAndBulkEvaluationRule(self.chainer, N))
general_evaluation_to_member_rules = create_general_evaluation_to_member_rules(
self.chainer)
for rule in general_evaluation_to_member_rules:
self.chainer.add_rule(rule)
# create probabilistic logical links out of MemberLinks
self.chainer.add_rule(AndEvaluationRule(self.chainer))
self.chainer.add_rule(OrEvaluationRule(self.chainer))
self.chainer.add_rule(ExtensionalLinkEvaluationRule(self.chainer))
self.chainer.add_rule(IntensionalLinkEvaluationRule(self.chainer))
self.chainer.add_rule(SubsetEvaluationRule(self.chainer))
self.chainer.add_rule(NegatedSubsetEvaluationRule(self.chainer))
self.chainer.add_rule(ExtensionalSimilarityEvaluationRule(
self.chainer))
self.chainer.add_rule(
IntensionalInheritanceEvaluationRule(self.chainer))
self.chainer.add_rule(IntensionalSimilarityEvaluationRule(
self.chainer))
self.chainer.add_rule(SatisfyingSetToConceptRule(self.chainer, 1))
# context rules
self.chainer.add_rule(InheritanceToContextRule(self.chainer))
self.chainer.add_rule(EvaluationToContextRule(self.chainer))
self.chainer.add_rule(SubsetToContextRule(self.chainer))
self.chainer.add_rule(ContextToInheritanceRule(self.chainer))
self.chainer.add_rule(ContextToEvaluationRule(self.chainer))
self.chainer.add_rule(ContextToSubsetRule(self.chainer))
# self.member_rules = [GeneralEvaluationToMemberRule(self.chainer),
# MemberToEvaluationRule(self.chainer)]
# self.member_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(MemberToInheritanceRule(self.chainer))
# self.chainer.add_rule(InheritanceToMemberRule(self.chainer))
# AttractionLink could be useful for causality
self.chainer.add_rule(AttractionRule(self.chainer))
self.chainer.add_rule(ScholemRule(self.chainer))
for rule in create_temporal_rules(self.chainer):
self.chainer.add_rule(rule)
boolean_transformation_rules = create_boolean_transformation_rules(
self.chainer)
for rule in boolean_transformation_rules:
self.chainer.add_rule(rule)
def lookup_rule(self, rule_schema_node):
rule = self.chainer.lookup_rule(rule_schema_node.name)
rule._chainer = self.test_chainer
return rule
class AllRules(object):
def __init__(self, atomspace, chainer):
from pln.rules import rules, temporal_rules, boolean_rules, quantifier_rules, context_rules, predicate_rules
# contains every rule
self.chainer = Chainer(atomspace,
stimulateAtoms=False,
agent=self,
learnRuleFrequencies=False)
# contains only some rules
self.test_chainer = chainer
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))
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))
for rule in temporal_rules.create_temporal_rules(self.chainer):
self.chainer.add_rule(rule)
def lookup_rule(self, rule_schema_node):
rule = self.chainer.lookup_rule(rule_schema_node.name)
rule._chainer = self.test_chainer
return rule
