def evaluateEpistemicState(self, epi):
kb = epi[self.target]
v = epi['V']
basicLogic.setkbfromv(kb, v)
# we are allowed to assume a monotonic logic base for this
for rule in kb:
if isinstance(rule, basicLogic.atom):
v = m_th_simplified.addVarAssignmentToV(
v, rule.getName(), True)
elif isinstance(rule, basicLogic.operator_monotonic_negation):
if isinstance(rule.clause, basicLogic.atom):
v = m_th_simplified.addVarAssignmentToV(
v, rule.clause.getName(), False)
elif isinstance(rule, basicLogic.operator_bitonic):
head = rule.clause1
body = rule.clause2
bodyVal = body.evaluate()
if isinstance(rule, basicLogic.operator_bitonic_implication):
if isinstance(head, basicLogic.atom):
if bodyVal != None:
v = m_th_simplified.addVarAssignmentToV(
v, head.getName(), bodyVal)
else:
print("unknown bitonic")
else:
print("unknown operation")
epi['V'] = v
epi[self.target] = kb
#remove all v assignments from this epi, th is not an evaluation function
for v in epi['V']:
for rule in epi[self.target]:
rule.deepSet(v.getName(), None)
return epi
def setTruth(epi):
S = epi['S']
V = epi['V']
ats = epi.getAtomNamesInStructuralVariables(['S'])
#Assign TRUTH in possible world
for rule in S:
#clear the interpretation
basicLogic.setkbfromv(epi['S'], V)
left = rule.clause1
right = rule.clause2
if left.getName() in ats:
evaluation = right.evaluate()
if evaluation == True:
m_semantic.changeAssignmentInV(left.getName(), evaluation,
V)
left = rule.clause2
right = rule.clause1
if left.getName() in ats:
evaluation = right.evaluate()
if evaluation == True:
m_semantic.changeAssignmentInV(left.getName(), evaluation,
V)
return epi
def setFalse(epi):
#I_V(S)
S = epi['S']
V = epi['V']
ats = epi.getAtomNamesInStructuralVariables(['S'])
#Assign TRUTH in possible world
for rule in S:
#clear the interpretation
epi['S'] = basicLogic.setkbfromv(epi['S'], V)
left = rule.clause1
right = rule.clause2
if left.getName() in ats:
evaluation = right.evaluate()
if evaluation == False:
shared = CognitiveOperation.getBodiesWhichShareHead(
left, epi['S'])
allFalse = True
for body in shared:
if body.evaluate() != False:
allFalse = False
if allFalse:
m_semantic.changeAssignmentInV(left.getName(),
evaluation, V)
left = rule.clause2
right = rule.clause1
if left.getName() in ats:
evaluation = right.evaluate()
if evaluation == False:
shared = CognitiveOperation.getBodiesWhichShareHead(
left, epi['S'])
allFalse = True
for body in shared:
if body.evaluate() != False:
allFalse = False
if allFalse:
m_semantic.changeAssignmentInV(left.getName(),
evaluation, V)
return epi
def f_turn(pi, observation):
finalStructures = pi.evaluate()
finalStates = StatePointOperations.flattenStatePoint(finalStructures)
if not isinstance(finalStates, list):
finalStates = [finalStates]
conditional = scpNotationParser.stringListToBasicLogic(
["( ( 3 | D ) or ( D' | 7 ) )"])
# we are willing to turn the card if either the (3 | D) or the contrapositive case ( D' | 7 ) holds
obs = scpNotationParser.stringListToBasicLogic(
['( {} <- T )'.format(observation)])
responses = []
for epi in finalStates:
#print ("\n")
#print (epi)
basicLogic.setkbfromv(obs, epi['V'])
#the conditional must be verified or falsified
#the observation must be True
allObsTrue = True
for o in obs:
if o.evaluate() != True:
allObsTrue = False
#allCondApplicable = True
if allObsTrue:
responses.append('observations hold')
else:
responses.append('observations do not')
#now we need to find the minimal set of abducibles to turn the cards
turnResponses = []
#WE PREFER MINIMAL EXPLANATIONS
for i in range(0, len(responses)):
if responses[i] == 'observations hold':
turnResponses.append(finalStates[i])
minimalSubset = []
for epi in turnResponses:
#print ("epi is ", epi)
#print (epi['R']['abducibles'])
#print ("and then here")
x = [
StatePointOperations.properSubset(ot['R']['abducibles'],
epi['R']['abducibles'])
for ot in turnResponses
]
#another least model is a subset of this one
if True in x:
pass
else:
minimalSubsetAsVariables = epi['V']
minimalSubsetAsList = StatePointOperations.VtoTupleList(
minimalSubsetAsVariables, ignoreNone=True)
if minimalSubsetAsList not in minimalSubset:
minimalSubset.append(minimalSubsetAsVariables)
turns = []
for mini in minimalSubset:
allCondApplicable = True
#this is done later
basicLogic.setkbfromv(conditional, mini)
#print ("mini is : ", mini)
for cond in conditional:
#print (cond, "Evaluates to ", cond.evaluate())
if cond.evaluate() == None:
#print ("evaluation was ", cond.evaluate())
allCondApplicable = False
#print ("cond is ",cond)
if allCondApplicable:
turns.append('Turn Card')
#print ("1")
else:
turns.append('Do Not Turn')
#print("2")
return turns
