def create_chainer(self, atomspace):
self.chainer = Chainer(atomspace,
stimulateAtoms=False,
preferAttentionalFocus=False,
allow_output_with_variables=True,
delete_temporary_variables=True)
for rule in 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)
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)
timeLinks = "opencog/spacetime/spacetime_types.scm"
data = "tests/python/test_pln/scm_disabled/temporal/temporalToyExample.scm"
# data = "opencog/python/pln_old/examples/temporal/temporal-r2l-input.scm"
# initialize atomspace
atomspace = AtomSpace()
__init__(atomspace)
for item in [coreTypes, utilities, timeLinks, data]:
load_scm(atomspace, item)
# initialize chainer
chainer = Chainer(atomspace,
stimulateAtoms=False,
allow_output_with_variables=True,
delete_temporary_variables=True)
for rule in create_temporal_rules(chainer):
chainer.add_rule(rule)
if print_starting_contents:
print('AtomSpace starting contents:')
atomspace.print_list()
outputs_produced = 0
for i in range(0, num_steps):
result = chainer.forward_step()
output = None
input = None
rule = None
if result is not None:
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 __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)
