class ForwardInferenceAgent(MindAgent):
def __init__(self):
self.chainer = None
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))
# for rule in temporal_rules.create_temporal_rules(self.chainer):
# self.chainer.add_rule(rule)
def run(self, atomspace):
# incredibly exciting futuristic display!
#import os
#os.system('cls' if os.name=='nt' else 'clear')
def show_atoms(atoms):
return ' '.join(str(atom) + str(atom.av) for atom in atoms)
if self.chainer is None:
self.create_chainer(atomspace)
# For simplicity, do nothing the first time. Silly APIs mean you have to call run to set the atomspace
return
# Update all of the node probabilities at each step
self.chainer.update_all_node_probabilities()
result = self.chainer.forward_step()
if result:
(rule, inputs, outputs) = result
print '==== Inference ===='
print rule.name, show_atoms(outputs), '<=', show_atoms(inputs)
#print
#print '==== Attentional Focus ===='
#for atom in get_attentional_focus(atomspace)[0:30]:
# print str(atom), atom.av
#print '==== Result ===='
#print output
#print '==== Trail ===='
#print_atoms( self.chainer.trails[output] )
else:
print 'Invalid inference attempted'
try:
pass
except AssertionError:
import sys, traceback
_, _, tb = sys.exc_info()
traceback.print_tb(tb) # Fixed format
tbInfo = traceback.extract_tb(tb)
filename, line, func, text = tbInfo[-1]
print('An error occurred on line ' + str(line) + ' in statement ' +
text)
exit(1)
except Exception, e:
print e
print e.args
e.print_traceback()
import sys, traceback
_, _, tb = sys.exc_info()
traceback.print_tb(tb) # Fixed format
tbInfo = traceback.extract_tb(tb)
filename, line, func, text = tbInfo[-1]
print('An error occurred on line ' + str(line) + ' in statement ' +
text)
exit(1)
class ForwardInferenceAgent(MindAgent):
def __init__(self):
self.chainer = None
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))
# for rule in temporal_rules.create_temporal_rules(self.chainer):
# self.chainer.add_rule(rule)
def run(self, atomspace):
# incredibly exciting futuristic display!
#import os
#os.system('cls' if os.name=='nt' else 'clear')
def show_atoms(atoms):
return ' '.join(str(atom)+str(atom.av) for atom in atoms)
if self.chainer is None:
self.create_chainer(atomspace)
# For simplicity, do nothing the first time. Silly APIs mean you have to call run to set the atomspace
return
# Update all of the node probabilities at each step
self.chainer.update_all_node_probabilities()
result = self.chainer.forward_step()
if result:
(rule, inputs, outputs) = result
print '==== Inference ===='
print rule.name, show_atoms(outputs), '<=', show_atoms(inputs)
#print
#print '==== Attentional Focus ===='
#for atom in get_attentional_focus(atomspace)[0:30]:
# print str(atom), atom.av
#print '==== Result ===='
#print output
#print '==== Trail ===='
#print_atoms( self.chainer.trails[output] )
else:
print 'Invalid inference attempted'
try:
pass
except AssertionError:
import sys,traceback
_,_,tb = sys.exc_info()
traceback.print_tb(tb) # Fixed format
tbInfo = traceback.extract_tb(tb)
filename,line,func,text = tbInfo[-1]
print ('An error occurred on line ' + str(line) + ' in statement ' + text)
exit(1)
except Exception, e:
print e
print e.args
e.print_traceback()
import sys,traceback
_,_,tb = sys.exc_info()
traceback.print_tb(tb) # Fixed format
tbInfo = traceback.extract_tb(tb)
filename,line,func,text = tbInfo[-1]
print ('An error occurred on line ' + str(line) + ' in statement ' + text)
exit(1)
