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)