def backchain_to_goal_tree(rules, hypothesis):
goal_tree = [hypothesis]
for rule in rules:
# take THEN part of a rule
consequent = rule.consequent()
for option in consequent:
bindings = match(option, hypothesis)
if bindings or option == hypothesis:
# take IF part of a rule
antecedent = rule.antecedent()
if isinstance(antecedent, str):
new_hypothesis = populate(antecedent, bindings)
goal_tree.append(backchain_to_goal_tree(rules, new_hypothesis))
goal_tree.append(new_hypothesis)
else:
statements = [populate(ante_expr, bindings) for ante_expr in antecedent]
new_goal_tree = []
for statement in statements:
new_goal_tree.append(backchain_to_goal_tree(rules, statement))
# check the leaves of AND-OR tree and recursively backward chain on them.
if isinstance(antecedent, AND):
goal_tree.append(AND(new_goal_tree))
else:
goal_tree.append(OR(new_goal_tree))
return simplify(OR(goal_tree))
def backchain_to_goal_tree(rules, hypothesis,myLeaf = []):
count = 0
for i in range(len(rules)):
bindings = (match(rules[i].consequent()[0],hypothesis))
if bindings is not None:
print 'type of bindings',bindings
andLeaf = AND()
orLeaf = OR()
for x in range(len(rules[i].antecedent())):
print 'in antecedent.....',rules[i].antecedent()[x]
node = populate((rules[i].antecedent()[x]),bindings)
if type(rules[i].antecedent()) == AND:
andLeaf.append(populate((rules[i].antecedent()[x]),bindings))
myLeaf.append(simplify(andLeaf))
print 'andLeaf', andLeaf
if type(rules[i].antecedent()) == OR:
orLeaf.append(populate((rules[i].antecedent()[x]),bindings))
myLeaf.append(simplify(orLeaf))
print 'orLeaf',orLeaf
disnode = backchain_to_goal_tree(rules,node)
else:
print 'hypothesis'
#myLeaf.append(hypothesis)
return simplify(myLeaf)
def backchain_to_goal_tree(rules, hypothesis, myLeaf=[]):
count = 0
for i in range(len(rules)):
bindings = (match(rules[i].consequent()[0], hypothesis))
if bindings is not None:
print 'type of bindings', bindings
andLeaf = AND()
orLeaf = OR()
for x in range(len(rules[i].antecedent())):
print 'in antecedent.....', rules[i].antecedent()[x]
node = populate((rules[i].antecedent()[x]), bindings)
if type(rules[i].antecedent()) == AND:
andLeaf.append(
populate((rules[i].antecedent()[x]), bindings))
myLeaf.append(simplify(andLeaf))
print 'andLeaf', andLeaf
if type(rules[i].antecedent()) == OR:
orLeaf.append(
populate((rules[i].antecedent()[x]), bindings))
myLeaf.append(simplify(orLeaf))
print 'orLeaf', orLeaf
disnode = backchain_to_goal_tree(rules, node)
else:
print 'hypothesis'
#myLeaf.append(hypothesis)
return simplify(myLeaf)
def backchain_to_goal_tree(rules, hypothesis):
new_hyp_list = []
goaltree = []
for rule in rules:
for word in rule.consequent():
if match(rule.consequent()[0], hypothesis) != None:
new_hyp = populate(rule.antecedent(),
match(rule.consequent()[0], hypothesis))
new_hyp_list += [new_hyp]
goaltree += [hypothesis]
for new_hyp in new_hyp_list:
goaltree += [new_hyp]
goaltree = OR(goaltree)
if len(goaltree) != 0:
for i in range(1, len(goaltree)):
if isinstance(goaltree[i], (list, tuple)):
for j in range(0, len(goaltree[i])):
goaltree[i][j] = simplify(
backchain_to_goal_tree(rules, goaltree[i][j]))
else:
goaltree[i] = simplify(
backchain_to_goal_tree(rules, goaltree[i]))
goaltree = simplify(goaltree)
return goaltree
def backchain_to_goal_tree(rules, hypothesis):
"""
Takes a hypothesis (string) and a list of rules (list
of IF objects), returning an AND/OR tree representing the
backchain of possible statements we may need to test
to determine if this hypothesis is reachable or not.
This method should return an AND/OR tree, that is, an
AND or OR object, whose constituents are the subgoals that
need to be tested. The leaves of this tree should be strings
(possibly with unbound variables), *not* AND or OR objects.
Make sure to use simplify(...) to flatten trees where appropriate.
"""
toReturnTrees = [hypothesis]
matches = False
for rule in rules:
for consequent in rule.consequent():
variables = match(consequent, hypothesis)
if variables == None:
continue
matches = True
# the thing you have to prove is true,
# in order to prove your hypothesis is true
antecedent = rule.antecedent(
) #populate(rule.antecedent(), variables)
# the subtree you will return
tree = None
if isinstance(antecedent, str):
tree = backchain_to_goal_tree(rules,
populate(antecedent, variables))
elif isinstance(antecedent, AND):
tree = AND([
backchain_to_goal_tree(rules, populate(clause, variables))
for clause in antecedent
])
elif isinstance(antecedent, OR):
tree = OR([
backchain_to_goal_tree(rules, populate(clause, variables))
for clause in antecedent
])
tree = simplify(tree)
toReturnTrees.append(tree)
if not matches:
return hypothesis
return simplify(simplify(OR([toReturnTrees])))
def backchain_to_goal_tree(rules, hypothesis):
tree = [hypothesis]
for rule in rules:
cons = rule.consequent()
antec = rule.antecedent()
#Busco "matches"
isInConsequent = False
for pattern in cons:
dictMatch = match(pattern,hypothesis)
if(dictMatch != None):
isInConsequent = True
#print "Match!"
#print rule
break
if(isInConsequent):
listaTemp = []
if(not isinstance(antec,list)): #Es una expresion simple
expP = populate(antec, dictMatch)
expP = backchain_to_goal_tree(rules, expP)
tree.append(expP)
else:
for exp in antec:
expP = populate(exp,dictMatch)
expP = backchain_to_goal_tree(rules,expP)
listaTemp.append(expP)
if(isinstance(antec,AND)):
tree.append(AND(listaTemp))
elif(isinstance(antec,OR)):
tree.append(OR(listaTemp))
return simplify(OR(tree))
def backchain_to_goal_tree(rules, hypothesis):
goal_tree = OR(hypothesis)
for rule in rules:
consequence_pattern = rule.consequent()[0]
bindings = match(consequence_pattern,hypothesis)
if bindings == None:
#print 'no rule was found'
continue
else:
if isinstance(rule.antecedent(),AND):
subtree = AND()
else:
subtree = OR()
if isinstance(rule.antecedent(),(OR,AND)):
for antecedent in rule.antecedent():
new_hypo = populate(antecedent,bindings)
subtree.append(backchain_to_goal_tree(rules,new_hypo))
goal_tree.append(subtree)
else:
new_hypo = populate(rule.antecedent(),bindings)
subtree.append(backchain_to_goal_tree(rules,new_hypo))
goal_tree.append(subtree)
return simplify(goal_tree)
def backchain_to_goal_tree(rules, hypothesis):
# print 'START backchain_to_goal_tree', hypothesis
goal_tree = OR()
matching_rules = find_matching_rules(rules, hypothesis)
#print "matching rules", matching_rules
if len(matching_rules) > 0:
goal_tree.append(hypothesis)
for rule in matching_rules:
consequent = rule.consequent()
antecedent = rule.antecedent()
for c in consequent:
bindings = match(c, hypothesis)
if bindings != None:
#print "consequent:", c
#print "antecedent:", antecedent
#print "bindings:", bindings
if isinstance(antecedent, (OR, AND)):
for i in xrange(len(antecedent)):
new_hypothesis = populate(antecedent[i], bindings)
#print "new hypothesis", new_hypothesis
antecedent[i] = backchain_to_goal_tree(
rules, new_hypothesis)
goal_tree.append(antecedent)
else:
new_hypothesis = populate(antecedent, bindings)
goal_tree.append(
backchain_to_goal_tree(rules, new_hypothesis))
goal_tree = simplify(goal_tree)
else:
goal_tree = hypothesis
return goal_tree
def backchain_to_goal_tree(rules, hypothesis):
"""
LHG Code from 6.034, modified for the ADE system.
Takes a hypothesis (string) and a list of rules (list
of IF objects), returning an AND/OR tree representing the
backchain of possible statements we may need to test
to determine if this hypothesis is reachable or not.
This method should return an AND/OR tree, that is, an
AND or OR object, whose constituents are the subgoals that
need to be tested. The leaves of this tree should be strings
(possibly with unbound variables), *not* AND or OR objects.
Make sure to use simplify(...) to flatten trees where appropriate.
"""
top_level = [hypothesis]
for rule in rules:
matching = match(rule.consequent(), hypothesis)
if matching is not None:
tree = []
next = populate(rule.antecedent(), matching)
ant_stuff = type(rule.antecedent())
if ant_stuff is str: # leaf
tree.append(backchain_to_goal_tree(rules, next))
else:
for hyp in next:
tree.append(backchain_to_goal_tree(rules, hyp))
if ant_stuff == OR:
top_level.append(OR(tree))
else: top_level.append(AND(tree))
return simplify(OR(top_level))
def backchain_to_goal_tree(rules, hypothesis):
anyMatches = False
results = OR(hypothesis)
for rule in rules:
for statement in rule.consequent():
matchOutput = match(statement, hypothesis)
if matchOutput !=None:
anyMatches = True
populatedAnt = populate(rule.antecedent(), matchOutput) #populate antecedent with vocab
if isinstance(populatedAnt,list):
ands = AND()
ors = OR()
for statement in populatedAnt: #populated Ant is and / or
if isinstance(populatedAnt,AND):
ands.append(backchain_to_goal_tree(rules,statement))
elif isinstance(populatedAnt,OR):
ors.append(backchain_to_goal_tree(rules,statement))
if len(ands)!=0:
results.append(ands)
elif len(ors)!=0:
results.append(ors)
else:
results.append(backchain_to_goal_tree(rules,populatedAnt))
if anyMatches == False:
results.append(OR(hypothesis))
return simplify(results)
def backchain_to_goal_tree(rules, hypothesis):
goal_tree = OR(hypothesis)
for rule in rules:
consequent = rule.consequent()[0]
matching = match(consequent, hypothesis)
if matching or matching == {}:
if isinstance(rule.antecedent(), AND):
leaf_tree = AND()
for antecedent in rule.antecedent():
if populate(antecedent, matching):
leaf_tree.append(
backchain_to_goal_tree(
rules, populate(antecedent, matching)))
goal_tree.append(leaf_tree)
else:
leaf_tree.append(
backchain_to_goal_tree(rules, antecedent))
elif isinstance(rule.antecedent(), OR):
leaf_tree = OR()
for antecedent in rule.antecedent():
if populate(antecedent, matching):
leaf_tree.append(
backchain_to_goal_tree(
rules, populate(antecedent, matching)))
goal_tree.append(leaf_tree)
else:
leaf_tree.append(
backchain_to_goal_tree(rules, antecedent))
else:
goal_tree.append(
backchain_to_goal_tree(
rules, populate(rule.antecedent(), matching)))
return simplify(goal_tree)
def backchain_to_goal_tree(rules, hypothesis):
goal_tree = OR()
goal_tree.append(hypothesis) # The hypothesis is the ultimate CSF
if not rules:
return list(goal_tree) # Bad base test? Not sure why the need to cast back to list
for rule in rules:
consequent = rule.consequent()[0] # should handle multiple?
bindings = match(consequent, hypothesis)
if bindings or bindings == {}:
antecedent = rule.antecedent()
if isinstance(antecedent, AND):
branch = AND()
for condition in antecedent:
cond = populate(condition, bindings)
branch.append(backchain_to_goal_tree(rules, cond))
goal_tree.append(branch)
elif isinstance(antecedent, OR):
branch = OR()
for condition in antecedent:
cond = populate(condition, bindings)
branch.append(backchain_to_goal_tree(rules, cond))
goal_tree.append(branch)
else: # leaf
leaf = populate(antecedent, bindings)
goal_tree.append(leaf)
goal_tree.append(backchain_to_goal_tree(rules, leaf)) # new leaf may match older consequent, need to check again
return simplify(goal_tree)
def backchain_to_goal_tree(rules, hypothesis):
hows = []
for rule in rules:
if isinstance(rule.consequent(), basestring):
cons = [rule.consequent()]
else:
cons = rule.consequent()
for c in rule.consequent():
m = match(c, hypothesis)
if m is not None:
instantiated_candidate = populate(c, m)
hows.append(instantiated_candidate)
antecedents_hows = []
if isinstance(rule.antecedent(), basestring):
ants = [rule.antecedent()]
else:
ants = rule.antecedent()
for ant in ants:
p_ant = populate(ant, m)
ant_hows = backchain_to_goal_tree(rules, p_ant)
antecedents_hows.append(ant_hows)
if isinstance(rule.antecedent(), basestring) or isinstance(rule.antecedent(), OR) :
for ant_hows in antecedents_hows:
hows.append(ant_hows)
elif isinstance(rule.antecedent(), AND):
hows.append(AND(*antecedents_hows))
else:
raise ValueError, "something is wront with this antecedent: %s" % rule.antecedent()
if not hows:
return hypothesis
else:
return simplify(OR(hows))
def backchain_to_goal_tree(rules, hypothesis):
result = OR(hypothesis)
for rule in rules:
b = []
hype = []
for con in rule.consequent():
if match(con, hypothesis) is not None:
b.append(match(con, hypothesis))
for i in b:
if i is not None and isinstance(rule.antecedent(), (AND, OR)):
for j in rule.antecedent():
if populate(j, i) is not None:
hype.append(populate(j, i))
elif i is not None:
if populate(rule.antecedent(), i) is not None:
hype.append(populate(rule.antecedent(), i))
if len(hype) > 0:
if isinstance(rule.antecedent(), AND):
result.append(AND())
for h in hype:
result[-1].append(backchain_to_goal_tree(rules, h))
elif isinstance(rule.antecedent(), OR):
result.append(OR())
for h in hype:
result[-1].append(backchain_to_goal_tree(rules, h))
else:
for h in hype:
result.append(backchain_to_goal_tree(rules, h))
result=simplify(result)
return result
def backchain_to_goal_tree(rules, hypothesis):
"""
Takes a hypothesis (string) and a list of rules (list
of IF objects), returning an AND/OR tree representing the
backchain of possible statements we may need to test
to determine if this hypothesis is reachable or not.
This method should return an AND/OR tree, that is, an
AND or OR object, whose constituents are the subgoals that
need to be tested. The leaves of this tree should be strings
(possibly with unbound variables), *not* AND or OR objects.
Make sure to use simplify(...) to flatten trees where appropriate.
"""
#creating the results of our hypothesis
results = [hypothesis]
#looping through all the rules (necessary for each new hypothesis under recursion)
for rule in rules:
#taking the binder variable
binder = match(rule.consequent(), hypothesis)
#seeing if there is actually a matching statement
if binder or rule.consequent() == hypothesis:
#collecting all the antecedents to a rule
antecedents = rule.antecedent()
#special case if there is only 1 antecedent
if isinstance(antecedents, str):
#resetting our hypothesis so we can recursively go
#through the one hypothesis
hypothesis = populate(antecedents, binder)
#have to append the hypothesis itself
results.append(hypothesis)
#recursively go through rules again with new hypothesis
results.append(backchain_to_goal_tree(rules, hypothesis))
else: #if the antecedent has more AND/OR statements (more depth)
hypotheses = [
populate(antecedent, binder) for antecedent in antecedents
]
#recursive results for each antecent(which became a new
#hypothesis)
sub_results = [
backchain_to_goal_tree(rules, hypothesis)
for hypothesis in hypotheses
]
#binding the sub_results correctly based on how the
#antecedents were stored
if isinstance(antecedents, AND):
results.append(AND(sub_results))
elif isinstance(antecedents, OR):
results.append(OR(sub_results))
#returning simplified version as we go
return simplify(OR(results))
def backchain_to_goal_tree(rules, hypothesis):
results = [hypothesis]
for rule in rules:
consequent = rule.consequent()
for expr in consequent:
bindings = match(expr, hypothesis)
if bindings or expr == hypothesis:
antecedent = rule.antecedent()
if isinstance(antecedent, str):
new_hypothesis = populate(expr, bindings)
results.append(
backchain_to_goal_tree(rules, new_hypothesis))
results.append(new_hypothesis)
else:
statements = [
populate(expr, bindings) for ante_expr in antecedent
]
new_results = []
for statement in statements:
new_results.append(
backchain_to_goal_tree(rules, statement))
results.append(create_statement(new_results, antecedent))
return simplify(results)
def backchain_to_goal_tree(rules, hypothesis):
ret = hypothesis
for rule in rules:
consequent = rule.consequent()
for conse in consequent:
bindings = match(conse, hypothesis)
if bindings != None:
antecedent = rule.antecedent()
if is_leaf_condition(antecedent):
condition = antecedent.__class__
tmp = []
for ante in antecedent:
new_hypothesis = populate(ante, bindings)
ans = backchain_to_goal_tree(rules, new_hypothesis)
tmp.append(ans)
ret = OR(ret, condition(tmp))
else:
new_hypothesis = populate(antecedent, bindings)
tmp = backchain_to_goal_tree(rules, new_hypothesis)
ret = OR(ret, tmp)
return simplify(ret)
def backchain_to_goal_tree(rules, hypothesis):
tree=OR(hypothesis)
#print "hypothesis: "+str(hypothesis)
matches=False
for rule in rules:
#print "rule: "+str(rule)
cons=rule.consequent()
ant=rule.antecedent()
#print "cons: "+str(cons)
for c in cons:
#print "c:"+str(c)
if match(c,hypothesis)!=None:
matches=True
if isinstance(ant, AND):
subtree=AND()
for a in ant:
subtree.append(backchain_to_goal_tree(rules,populate(a, match(c,hypothesis))))
elif isinstance(ant, OR):
subtree=OR()
for a in ant:
subtree.append(backchain_to_goal_tree(rules,populate(a,match(c,hypothesis))))
else:
subtree=backchain_to_goal_tree(rules, populate(ant,match(c,hypothesis)))
tree.append(subtree)
if not(match):
tree.append(hypothesis)
return simplify(tree)
def backchain_to_goal_tree(rules, hypothesis):
length = len(rules)
if length == 0:
return hypothesis
tree = OR()
for element in rules:
con = element.consequent()
mat = match(con[0], hypothesis)
if mat is not None and len(mat) >= 0:
antec = element.antecedent()
if isinstance(antec, list):
sub = AND()
if isinstance(antec, OR): sub = OR()
for x in antec:
new_tree = backchain_to_goal_tree(rules, populate(x, mat))
sub.append(new_tree)
tree.append(sub)
else:
new_tree = backchain_to_goal_tree(rules, populate(antec, mat))
tree.append(AND(new_tree))
else:
tree.append(hypothesis)
new = simplify(tree)
return new
def backchain_to_goal_tree(rules, hypothesis):
# for rule in rules:
# print rule.antecedent()
# print rule.consequent()
if not rules:
return hypothesis
return simplify(rule_match_goal_tree(rules, hypothesis))
def backchain_to_goal_tree(rules, hypothesis):
"""
Takes a hypothesis (string) and a list of rules (list
of IF objects), returning an AND/OR tree representing the
backchain of possible statements we may need to test
to determine if this hypothesis is reachable or not.
This method should return an AND/OR tree, that is, an
AND or OR object, whose constituents are the subgoals that
need to be tested. The leaves of this tree should be strings
(possibly with unbound variables), *not* AND or OR objects.
Make sure to use simplify(...) to flatten trees where appropriate.
"""
matched = list([hypothesis])
for rule in rules:
bound_conseq = match(rule.consequent(), hypothesis)
if bound_conseq is not None:
new_antes = list()
if isinstance(rule.antecedent(), str):
bound_ante = populate(rule.antecedent(), bound_conseq)
matched.append(bound_ante)
matched.append(backchain_to_goal_tree(rules, bound_ante))
for ant in rule.antecedent():
new_antes.append(
backchain_to_goal_tree(rules, populate(ant, bound_conseq)))
if isinstance(rule.antecedent(), AND):
matched.append(AND(new_antes))
if isinstance(rule.antecedent(), OR):
matched.append(OR(new_antes))
return simplify(OR(matched))
def backchain_to_goal_tree(rules, hypothesis):
hypothesis = OR(hypothesis)
for rule in rules:
var_list = match(
rule.consequent()[0], hypothesis[0]
) #assume that rule.consequent() and hypothesis have exactly one string
if var_list == None:
continue
else:
if isinstance(rule.antecedent(), basestring):
appendage = [
backchain_to_goal_tree(
rules, populate(rule.antecedent(), var_list))
]
else:
appendage = [
backchain_to_goal_tree(rules, populate(data, var_list))
for data in rule.antecedent()
]
if isinstance(rule.antecedent(), AND):
appendage = AND(appendage)
else:
#catch if class= basestring also, because it will be removed in simplify()
appendage = OR(appendage)
hypothesis.append(appendage)
return simplify(hypothesis)
def backchain_to_goal_tree(rules, hypothesis):
treeNode = OR(hypothesis)
#For every rule in the tree, see which ones have consequents that match my hypothesis
matches = []
#iterate through every rule and collect the ones whose consequents match my hypothesis
for rule in rules:
#Fill in each consequent of the rule with the correct name of the antecendents
for consequent in rule.consequent():
matchAttempt = match(consequent, hypothesis)
#If we can match one of the consequents, we want to fill in the atecedents with the appropriate hypothesis
if(matchAttempt != None and not rule in matches):
for i, expression in enumerate(rule.antecedent()):
rule.antecedent()[i] = populate(expression, matchAttempt)
matches.append(rule)
#At this point we have a list of the rules that match, and all of the antecendents are filled in with the variable names we can fill them in with
treeNode = OR(hypothesis)
for ruleMatch in matches:
antecedent = ruleMatch.antecedent()
if( isinstance(antecedent, AND)):
node = AND()
else:
node = OR()
for newHypothesis in antecedent:
node.append(backchain_to_goal_tree(rules, newHypothesis))
treeNode.append(node)
return simplify(treeNode)
def backchain_to_goal_tree(rules, hypothesis):
"""
Takes a hypothesis (string) and a list of rules (list
of IF objects), returning an AND/OR tree representing the
backchain of possible statements we may need to test
to determine if this hypothesis is reachable or not.
This method should return an AND/OR tree, that is, an
AND or OR object, whose constituents are the subgoals that
need to be tested. The leaves of this tree should be strings
(possibly with unbound variables), *not* AND or OR objects.
Make sure to use simplify(...) to flatten trees where appropriate.
"""
tree = OR(hypothesis)
for rule in rules:
match_res = match(rule.consequent(), hypothesis)
if match_res is not None:
or_node = OR()
tree.append(or_node)
nodes = populate(rule.antecedent(), match_res)
if type(nodes) == str:
nodes = [nodes]
for i in range(len(nodes)):
nodes[i] = backchain_to_goal_tree(rules, nodes[i])
or_node.append(nodes)
tree = simplify(tree)
return tree
def backchain_to_goal_tree(rules, hypothesis):
# print ("backchain_to_goal_tree", rules, hypothesis)
result = OR(hypothesis)
for rule in rules:
for then in rule.consequent():
m = match(then, hypothesis)
if m == None:
continue
if isinstance(rule.antecedent(), AND):
new_rule = AND([
backchain_to_goal_tree(rules, populate(pattern, m))
for pattern in rule.antecedent()
])
result.append(new_rule)
elif isinstance(rule.antecedent(), OR):
new_rule = OR([
backchain_to_goal_tree(rules, populate(pattern, m))
for pattern in rule.antecedent()
])
result.append(new_rule)
elif isinstance(rule.antecedent(), str):
new_rule = OR(
backchain_to_goal_tree(rules,
populate(rule.antecedent(), m)))
result.extend(new_rule)
else:
print("backchain_to_goal_tree confusing antecedent",
rule.antecedent)
return None
return simplify(result)
def backchain_to_goal_tree(rules, hypothesis):
"""
Takes a hypothesis (string) and a list of rules (list
of IF objects), returning an AND/OR tree representing the
backchain of possible statements we may need to test
to determine if this hypothesis is reachable or not.
This method should return an AND/OR tree, that is, an
AND or OR object, whose constituents are the subgoals that
need to be tested. The leaves of this tree should be strings
(possibly with unbound variables), *not* AND or OR objects.
Make sure to use simplify(...) to flatten trees where appropriate.
"""
tree = OR(hypothesis)
for rule in rules:
variables = match(rule.consequent(), hypothesis)
if variables is not None:
if isinstance(rule.antecedent(), OR):
tree.append(OR([ backchain_to_goal_tree(rules, ant) for ant in populate(rule.antecedent(), variables)]))
elif isinstance(rule.antecedent(), AND):
tree.append(AND([ backchain_to_goal_tree(rules, ant) for ant in populate(rule.antecedent(), variables)]))
else:
tree.append(backchain_to_goal_tree(rules, populate(rule.antecedent(), variables)))
return simplify(tree)
def backchain_to_goal_tree(rules, hypothesis):
"""
Takes a hypothesis (string) and a list of rules (list
of IF objects), returning an AND/OR tree representing the
backchain of possible statements we may need to test
to determine if this hypothesis is reachable or not.
This method should return an AND/OR tree, that is, an
AND or OR object, whose constituents are the subgoals that
need to be tested. The leaves of this tree should be strings
(possibly with unbound variables), *not* AND or OR objects.
Make sure to use simplify(...) to flatten trees where appropriate.
"""
tree = OR(hypothesis)
for rule in rules:
bounded = match(rule.consequent(), hypothesis) #check if rule can produce the hypothesis
if bounded != None:
new_antecedent = populate(rule.antecedent(), bounded) #populate the vars of the rule's antecdent
#recurse on antecedent
if type(new_antecedent) == str:
tree.append(backchain_to_goal_tree(rules,new_antecedent))
else:
for i,new_hypothesis in enumerate(new_antecedent):
new_antecedent[i] = backchain_to_goal_tree(rules,new_hypothesis)
tree.append(new_antecedent)
return simplify(tree)
def backchain_to_goal_tree(rules, hypothesis):
"""
Takes a hypothesis (string) and a list of rules (list
of IF objects), returning an AND/OR tree representing the
backchain of possible statements we may need to test
to determine if this hypothesis is reachable or not.
This method should return an AND/OR tree, that is, an
AND or OR object, whose constituents are the subgoals that
need to be tested. The leaves of this tree should be strings
(possibly with unbound variables), *not* AND or OR objects.
Make sure to use simplify(...) to flatten trees where appropriate.
"""
tree = OR(hypothesis)
if len(rules)==0:
return hypothesis
for rule in rules:
if match(rule.consequent()[0], hypothesis) is not None:
popRule = populate(rule.antecedent(), match(rule.consequent()[0], hypothesis))
if isinstance(popRule, (AND, OR)):
for i in xrange(len(popRule)):
popRule[i]=backchain_to_goal_tree(rules, popRule[i])
tree.append(popRule)
else:
new_tree=backchain_to_goal_tree(rules, popRule)
tree.append(new_tree)
return simplify(tree)
def backchain_to_goal_tree(rules, hypothesis):
# find rules that produces this hypothesis
rules_con = [hypothesis]
for rule in rules:
for con in rule.consequent():
bindings = match(con, hypothesis)
if (bindings != None):
# this rule can result in the hypo
ant = rule.antecedent()
if (isinstance(ant, AND)):
rules_con.append(
AND([
backchain_to_goal_tree(rules,
populate(branch, bindings))
for branch in ant
]))
elif (isinstance(ant, OR)):
rules_con.append(
OR([
backchain_to_goal_tree(rules,
populate(branch, bindings))
for branch in ant
]))
else:
rules_con.append(
backchain_to_goal_tree(rules, populate(ant, bindings)))
return simplify(OR(rules_con))
def backchain_to_goal_tree(rules, hypothesis):
ors = [hypothesis]
for rule in rules:
consequents = rule.consequent()
for consequent in consequents:
matched_vars = match(consequent, hypothesis)
# matched_vars match
if matched_vars or consequent == hypothesis:
antecedent = rule.antecedent()
if isinstance(antecedent, str):
populated = populate(antecedent, matched_vars)
ors.append(populated)
ors.append(backchain_to_goal_tree(rules, populated))
else:
ante_rules = [
populate(rule, matched_vars) for rule in antecedent
]
ante_res = [
backchain_to_goal_tree(rules, ante_rule)
for ante_rule in ante_rules
]
if isinstance(antecedent, AND):
ors.append(AND(ante_res))
elif isinstance(antecedent, OR):
ors.append(OR(ante_res))
return simplify(OR(ors))
def backchain_to_goal_tree(rules, hypothesis):
"""
Takes a hypothesis (string) and a list of rules (list
of IF objects), returning an AND/OR tree representing the
backchain of possible statements we may need to test
to determine if this hypothesis is reachable or not.
This method should return an AND/OR tree, that is, an
AND or OR object, whose constituents are the subgoals that
need to be tested. The leaves of this tree should be strings
(possibly with unbound variables), *not* AND or OR objects.
Make sure to use simplify(...) to flatten trees where appropriate.
"""
hyp = OR([hypothesis])
for rule in rules:
# for consequent in rule.consequent():
m = match(rule.consequent(), hypothesis)
if m is not None:
# good_consequent = populate(consequents, m)
antecedents = rule.antecedent()
if isinstance(antecedents, str):
antecedents = [antecedents]
antecedent = [
backchain_to_goal_tree(rules, populate(a, m))
for a in antecedents
]
if isinstance(antecedents, OR):
hyp.append(OR(antecedent))
else:
hyp.append(AND(antecedent))
return simplify(hyp)
def backchain_to_goal_tree(rules, hypothesis):
matched_rules = []
results = []
for rule in rules:
rule_str = rule.consequent()[0]
if match(rule_str, hypothesis) is not None:
res = match(rule_str, hypothesis)
matched_rules.append(populate(rule.antecedent(), res))
for m in matched_rules:
if isinstance(m, AND):
res = []
for item in m:
res.append(backchain_to_goal_tree(rules, item))
results.append(AND(res))
elif isinstance(m, OR):
res = []
for item in m:
res.append(backchain_to_goal_tree(rules, item))
results.append(OR(res))
elif isinstance(m, str):
results.append(backchain_to_goal_tree(rules, m))
else:
raise Exception, "Not sure what do do with object '{}'".format(m)
return simplify(OR([hypothesis] + results))
def backchain_to_goal_tree(rules, hypothesis):
"""
Takes a hypothesis (string) and a list of rules (list
of IF objects), returning an AND/OR tree representing the
backchain of possible statements we may need to test
to determine if this hypothesis is reachable or not.
This method should return an AND/OR tree, that is, an
AND or OR object, whose constituents are the subgoals that
need to be tested. The leaves of this tree should be strings
(possibly with unbound variables), *not* AND or OR objects.
Make sure to use simplify(...) to flatten trees where appropriate.
"""
results=[hypothesis]
for rule in rules:
consequent=rule.consequent()
for expr in consequent:
bindings=match(expr,hypothesis)
if bindings or expr == hypothesis:
antecedent=rule.antecedent()
if isinstance(antecedent,str):
new_hypothesis=populate(antecedent,bindings)
results.append(backchain_to_goal_tree(rules,new_hypothesis))
results.append(new_hypothesis)
else:
statements=[populate(new_antecedent,bindings) for new_antecedent in antecedent]
new_results=[]
for statement in statements:
new_results.append(backchain_to_goal_tree(rules,statement))
if isinstance (antecedent, AND):
results.append(AND(new_results))
elif isinstance(antecedent,OR):
results.append(OR(new_results))
return simplify(OR(results))
def backchain_to_goal_tree(rules, hypothesis):
goal_tree = [hypothesis]
for rule in rules:
for consequent in rule.consequent(): # Extend from python list
bound_variables = match(consequent, hypothesis)
if bound_variables is not None: # We have matched expressions
antecedent_list = rule.antecedent()
if isinstance(antecedent_list,
RuleExpression): # Complex expression
if isinstance(antecedent_list, AND):
inner_goal_tree = AND([
backchain_to_goal_tree(
rules, populate(expr, bound_variables))
for expr in antecedent_list
])
elif isinstance(antecedent_list, OR):
inner_goal_tree = OR([
backchain_to_goal_tree(
rules, populate(expr, bound_variables))
for expr in antecedent_list
])
goal_tree.append(inner_goal_tree)
else: # just a string, which is a single leaf node
new_hypothesis = populate(antecedent_list, bound_variables)
goal_tree.append(
OR(backchain_to_goal_tree(rules, new_hypothesis)))
return simplify(OR(goal_tree))
def find_ante(rules, hypothesis):
antecedents = [hypothesis]
for rule in rules:
result = match(rule.consequent(), hypothesis)
if result != None:
ante = populate(rule.antecedent(), result)
if isinstance(ante, AND):
antecedents.append(
simplify(AND([find_ante(rules, cond)
for cond in ante])))
elif isinstance(ante, OR):
antecedents.append(
simplify(OR([find_ante(rules, cond)
for cond in ante])))
else:
antecedents.append(find_ante(rules, ante))
return simplify(OR(antecedents))
def backchain_to_goal_tree(rules, hypothesis):
rule_match = []
for rule in rules:
value = match(rule.consequent()[0], hypothesis)
if value is not None:
rule_match.append(rule)
# print rule
else:
pass
if len(rule_match) == 0:
return hypothesis
else:
or_add = OR(hypothesis)
for rule in rule_match:
vars = match(rule.consequent()[0], hypothesis)
if isinstance(rule.antecedent(), OR):
for idx, ant in enumerate(rule.antecedent()):
add_or = backchain_to_goal_tree(
rules, populate(ant, vars))
if idx == 0:
or_lst = OR(add_or)
else:
or_lst = OR(or_lst, add_or)
or_lst = simplify(or_lst)
or_add = OR(or_add, or_lst)
elif isinstance(rule.antecedent(), AND):
for idx, ant in enumerate(rule.antecedent()):
add_and = backchain_to_goal_tree(
rules, populate(ant, vars))
if idx == 0:
and_lst = AND(add_and)
else:
and_lst = AND(and_lst, add_and)
and_lst = simplify(and_lst)
or_add = OR(or_add, and_lst)
else:
for idx, ant in enumerate([rule.antecedent()]):
add_and = backchain_to_goal_tree(
rules, populate(ant, vars))
or_add = OR(or_add, add_and)
return simplify(or_add)
def backchain_to_goal_tree(rules, hypothesis):
rule_match = []
for rule in rules:
value = match(rule.consequent()[0], hypothesis)
if value is not None:
rule_match.append(rule)
# print rule
else:
pass
if len(rule_match) == 0:
return hypothesis
else:
or_add = OR(hypothesis)
for rule in rule_match:
vars = match(rule.consequent()[0], hypothesis)
if isinstance(rule.antecedent(), OR):
for idx, ant in enumerate(rule.antecedent()):
add_or = backchain_to_goal_tree(rules, populate(ant, vars))
if idx == 0:
or_lst = OR(add_or)
else:
or_lst = OR(or_lst, add_or)
or_lst = simplify(or_lst)
or_add = OR(or_add, or_lst)
elif isinstance(rule.antecedent(), AND):
for idx, ant in enumerate(rule.antecedent()):
add_and = backchain_to_goal_tree(rules,
populate(ant, vars))
if idx == 0:
and_lst = AND(add_and)
else:
and_lst = AND(and_lst, add_and)
and_lst = simplify(and_lst)
or_add = OR(or_add, and_lst)
else:
for idx, ant in enumerate([rule.antecedent()]):
add_and = backchain_to_goal_tree(rules,
populate(ant, vars))
or_add = OR(or_add, add_and)
return simplify(or_add)
def backchain_to_goal_tree(rules, hypothesis):
#print "\n", "HYPOTHESIS", hypothesis
new_hypothesis = []
variable = ""
for rule in rules:
for cons in rule.consequent():
if match(cons, hypothesis) != None:
new_hypothesis.append(rule.antecedent())
variable = match(cons, hypothesis)
#print "match!", cons, rule.antecedent()
#print "variable", variable
#print "new hypothesis", new_hypothesis
if len(new_hypothesis) < 1:
return hypothesis
branches = [hypothesis]
for antec in new_hypothesis:
expr = populate(antec, variable)
#print antec, variable, populate(antec, variable)
if isinstance (expr, str):
minitree = backchain_to_goal_tree(rules, expr)
else:
branchez = []
for argg in expr:
branchez.append(backchain_to_goal_tree(rules, argg))
if isinstance (expr, OR):
minitree = simplify(OR(branchez))
elif isinstance (expr, AND):
minitree = simplify(AND(branchez))
branches.append(minitree)
#print " TREE", branches
tree = simplify(OR(branches))
return tree
def backchain_antecedents(rules, bindings, rule): tree = [] antecedents = rule.antecedent() if isinstance(antecedents, str): antecedents = AND(antecedents) for antecedent in antecedents: branch = simplify(backchain_support(rules, populate(antecedent, bindings))) if branch is None: tree.append(populate(antecedent, bindings)) else: tree.append(branch) if isinstance(rule.antecedent(), AND): return simplify(AND(tree)) else: return simplify(OR(tree))
def backchain_to_goal_tree(rules, hypothesis):
matches = [hypothesis]
for rule in rules:
var_names = match(rule.consequent()[0], hypothesis)
if var_names is not None:
temp = copy.deepcopy(rule.antecedent())
if isinstance(temp, list):
for i in range(len(temp)):
temp[i] = simplify(
backchain_to_goal_tree(rules,
populate(temp[i], var_names)))
matches.append(simplify(temp))
else:
matches.append(
simplify(
populate(backchain_to_goal_tree(rules, temp),
var_names)))
return simplify(OR(matches))
def backchain_to_goal_tree(rules, hypothesis):
result2 = [hypothesis]
# while set[newh]!= set([hypothesis]):
for condition in rules:
resuls1 = []
if isinstance(condition, IF):
result = condition.antecedent()
condition = condition.consequent()
data = match(condition[0], hypothesis)
if data is not None:
# print(type(result))
if not isinstance(result, list):
result = [result]
for r in result:
if isinstance(r, OR):
for i in range(len(r)):
result3 = []
newh = populate(r[i], data)
# result3.append(newh)
result3.append(backchain_to_goal_tree(rules, newh))
resuls1.append(OR(result3))
elif isinstance(r, AND):
for i in range(len(r)):
result3 = []
newh = populate(r[i], data)
# result3.append(newh)
result3.append(backchain_to_goal_tree(rules, newh))
resuls1.append(AND(result3))
else:
newh = populate(r, data)
# resuls1.append(newh)
# print(newh)
newresult = backchain_to_goal_tree(rules, newh)
print(newresult)
resuls1.append(newresult)
if isinstance(result, AND):
result2.append(AND(resuls1))
else:
result2.append(OR(resuls1))
print(simplify(OR(result2)))
return simplify(OR(result2))
def backchain_to_goal_tree(rules, hypothesis):
result = []
for rule in rules:
matched = match(rule.consequent()[0], hypothesis)
if matched != None:
antecedent = rule.antecedent()
result.append(iterate_node(rules, antecedent, matched))
if len(result) > 0:
return simplify(OR(hypothesis, OR(result)))
else:
return hypothesis
def backchain_to_goal_tree(rules, hypothesis):
#print "Rules: ",rules
print "H: ",hypothesis
#if hypothesis=='zot':
#print "Rules: ",rules
#nodes=[rule.antecedent() for rule in rules if (match(rule.consequent()[0],hypothesis) or match=={})]
#print nodes
a={}
nodes=[]
for rule in rules:
#print "Consequent: ",rule.consequent()[0]
d=match(rule.consequent()[0],hypothesis)
#print d
if d or d=={}:
#print "MATCHED!!"
nodes.append(rule.antecedent())
#print rule.antecedent()
#print nodes
test_match=match(rule.consequent()[0],hypothesis)
if test_match:
a = dict(a.items()+test_match.items())
print a
tree=OR(hypothesis)
for node in nodes:
print "type: ",type(node)
if type(node) is OR or type(node) is AND:
print "\n\nAppending list of nodes: ",[backchain_to_goal_tree(rules, populate(elem,a)) for elem in node]
tree.append(type(node)([backchain_to_goal_tree(rules, populate(elem,a)) for elem in node]))
else:
print "\n\nAppending node: ",backchain_to_goal_tree(rules,populate(node,a))
tree.append(backchain_to_goal_tree(rules,populate(node,a)))
print "\n\n#####TREE!!\n",simplify(tree)
return simplify(tree)
def backchain_support(rules, hypothesis): tree = [] for rule in rules: for consequent in rule.consequent(): bindings = match(consequent, hypothesis) if bindings is not None: tree.append(populate(consequent, bindings)) tree.append(backchain_antecedents(rules, bindings, rule)) if len(tree) is not 0: return simplify(OR(tree))
def backchain_to_goal_tree(rules, hypothesis):
root = OR(hypothesis)
for rule in rules:
for consequent in rule.consequent():
matchResult = match(consequent, hypothesis)
if matchResult != None:
antecedent = rule.antecedent()
newHypothesis = AND() if isinstance(antecedent, AND) else OR()
if isinstance(antecedent, str):
antecedent = [antecedent]
for part in antecedent:
newHypothesis.append(backchain_to_goal_tree(rules, populate(part, matchResult)))
root.append(newHypothesis)
return simplify(root)
def expand_subtree(subtree, bindings, rules):
# build an identical subtree by recursively
# walking and replacing each leaf with a backchained
# tree
cloned_node = subtree.__class__()
for expression in subtree:
if isinstance(expression, RuleExpression):
cloned_node.append( expand_subtree(expression, bindings) )
else:
# assume if it's not a RuleExpression, it's a leaf node
hypothesis = populate(expression, bindings)
cloned_node.append( backchain_to_goal_tree(rules, hypothesis) )
return simplify( cloned_node )
def backchain_to_goal_tree(rules, hypothesis):
print "backchain_to_goal_tree", hypothesis
possible = [hypothesis]
for rule in rules:
print "rule", rule
for consequence in rule.consequent():
bindings = match(consequence, hypothesis)
if bindings != None:
print "bindings", bindings
sub_hypothesis = populate(rule.antecedent(), bindings)
print "sub_hypothesis", sub_hypothesis
sub_hypothesis_extended = extendGoalTree(rules, sub_hypothesis)
possible.append(sub_hypothesis_extended)
return simplify(OR(possible) )
def backchain_to_goal_tree(rules, hypothesis, toprint = 0):
# Found git entry from junoon53. Outlined flow. Recreating from flow.
goal_tree = [hypothesis]
if toprint >=1 : clno = getframeinfo(currentframe()).lineno;funcDebug({"\n Hypothesis is: ":hypothesis,"\n Goal tree is: ":goal_tree},clno)
for rule in rules:
consequent = rule.consequent()
if toprint >= 1: clno = getframeinfo(currentframe()).lineno;funcDebug({"\n Rule is ":rule,"\n Consequent is ":consequent},clno)
for expr in consequent:
bindings = match(expr,hypothesis)
if toprint >= 1: clno = getframeinfo(currentframe()).lineno;funcDebug({"\n expr is ":expr,"\n bindings is ":bindings},clno)
# if bindings is not equal to None, which is return by match when no match is made,
# or if the expression retrieved from the consequent of the rule is equal to the
# hypothesis, retrieve the antecedents of the rule for tests and recursion. Otherwise,
# fetch the next expression.
if bindings or expr == hypothesis:
antecedent = rule.antecedent()
# Is the antecedent a string? If so we can recurse on it as a new hypothesis.
# If not it must be a list of expressions that we will have to parse.
if isinstance(antecedent,str):
new_hypo = populate(antecedent,bindings)
goal_tree.append(backchain_to_goal_tree(rules,new_hypo))
# Once recursion completes and the results of that recursion are
# appended to the goal tree, we can append the new_hypo to the goal tree.
goal_tree.append(new_hypo)
else:
# Assuming antecedent is a list of expressions, retrieve them and assemble a list
# of statements to be acted on
# junoon53 used a list comprehension here but I'm going to write out the whole for
# loop just to make sure I know what is going on.
statements = []
new_goal_tree=[]
for anteEx in antecedent:
statements.append(populate(anteEx,bindings))
for statement in statements:
new_goal_tree.append(backchain_to_goal_tree(rules,statement))
goal_tree.append(createStatement(new_goal_tree,antecedent))
return simplify(OR(goal_tree))
def backchain_to_goal_tree(rules, hypothesis):
matching_rules = []
runningOR = OR()
for rule in rules:
match_result = match(rule._action[0], hypothesis)
if match_result != None:
runningOR.append(populate(rule._action[0], match_result))
conditional = rule._conditional
runningCondition = AND()
for condition in range(0, len(conditional)):
condition2 = populate(conditional[condition], match_result)
runningCondition.append(backchain_to_goal_tree(rules, condition2))
runningOR.append(runningCondition)
if len(runningOR) != 0:
return simplify(runningOR)
else:
return hypothesis
def backchain_to_goal_tree(rules, hypothesis): or_conditionals=[hypothesis] for rule in rules: for consequent in list(rule.consequent()): matches = match(consequent,hypothesis) if matches!=None: antecedent = rule.antecedent() if isinstance(antecedent,AND) or isinstance(antecedent,OR): sub_tree = [backchain_to_goal_tree(rules,populate(element,matches)) for element in list(antecedent)] if isinstance(antecedent,AND): or_conditionals.append(AND(sub_tree)) else: or_conditionals.append(OR(sub_tree)) else: or_conditionals.append(backchain_to_goal_tree(rules,populate(antecedent,matches))) return simplify(OR(or_conditionals)) raise NotImplementedError
def backchain_to_goal_tree(rules, hypothesis):
matchedRules = []
orExpression = OR(hypothesis)
for rule in rules:
for expression in rule.consequent():
thisMatch = match(expression, hypothesis)
if(not thisMatch is None):
ruleExpression = orExpression
antecedant = rule.antecedent()
if(isinstance(antecedant, RuleExpression)):
if(isinstance(antecedant, AND)):
andExpression = AND()
orExpression.append(andExpression)
ruleExpression = andExpression
for antecedantExpression in antecedant:
ruleExpression.append(backchain_to_goal_tree(rules, populate(antecedantExpression, thisMatch)))
else:
orExpression.append(populate(backchain_to_goal_tree(rules, antecedant), thisMatch))
return simplify(orExpression)
def backchain_to_goal_tree(rules, hypothesis):
# check rules for matching consequents
# i) no matches -> hypothesis is a leaf
# ii) matches -> OR(antecedent)
or_cond = OR()
# always append the hypothesis
or_cond.append(hypothesis)
for rule in rules:
bindings = match(rule.consequent()[0], hypothesis)
if bindings:
# append the goal tree from the antecedent
ante = rule.antecedent()
if isinstance(ante, RuleExpression):
or_cond.append( expand_subtree(ante, bindings, rules) )
return simplify( or_cond )
def backchain_to_goal_tree(rules, hypothesis):
results = [hypothesis]
for rule in rules:
consequent = rule.consequent()
for expr in consequent:
bindings = match(expr, hypothesis)
if bindings or expr == hypothesis:
antecedent = rule.antecedent()
if isinstance(antecedent, str):
new_hypothesis = populate(antecedent, bindings)
results.append(backchain_to_goal_tree(rules, new_hypothesis))
results.append(new_hypothesis)
else:
statements = [populate(ante_expr, bindings) for ante_expr in antecedent]
new_results = []
for statement in statements:
new_results.append(backchain_to_goal_tree(rules, statement))
results.append(create_statement(new_results, antecedent))
return simplify(OR(results))
def backchain_to_goal_tree(rules, hypothesis):
"""
#Try 3
tree = [hypothesis]
for rule in rules:
for exp in rule.consequent():
matching = match(exp, hypothesis)
if matching !=None:
antecedent = rule.antecedent()
if type(antecedent) is str:
newHyp = populate(antecedent, matching)
tree.append(backchain_to_goal_tree(rules, newHyp))
tree.append(newHyp)
else:
statements = [populate(exp, matching) for exp in antecedent]
newTree = []
for statement in statements:
newTree.append(backchain_to_goal_tree(rules, statement))
if type(antecedent) == type(OR()):
tree.append(OR(newTree))
else:
tree.append(AND(newTree))
return simplify(OR(tree))
"""
#Try 2
tree = [hypothesis]
for rule in rules:
for exp in rule.consequent():
matching = match(exp, hypothesis)
if matching != None:
filledAnts = populate(rule.antecedent(), matching)
if type(filledAnts) == type(AND()):
andState = AND([backchain_to_goal_tree(rules, state) for state in filledAnts])
tree.append(andState)
elif type(filledAnts) == type(OR()):
orState = OR([backchain_to_goal_tree(rules, state) for state in filledAnts])
tree.append(orState)
else:
tree.append(backchain_to_goal_tree(rules, filledAnts))
return simplify(OR(tree))
def backchain_to_goal_tree(rules, hypothesis):
goal_tree = OR(hypothesis)
for rule in rules:
for expr in rule.consequent():
bindings = match(expr, hypothesis)
if bindings or bindings == {}: # we have a match
antecedent = rule.antecedent()
subtree = AND() if isinstance(antecedent, AND) else OR()
if isinstance(antecedent, str):
new_nodes = [populate(antecedent, bindings)]
else:
new_nodes = [populate(subexpr, bindings) for subexpr in antecedent]
for new_node in new_nodes:
subtree.append(backchain_to_goal_tree(rules, new_node))
goal_tree.append(subtree)
return simplify(goal_tree)
def backchain_to_goal_tree(rules, hypothesis):
or_condition = []
no_match = True
for rule in rules:
consequents = rule.consequent()
if not isinstance(consequents, list):
consequents = [consequents]
for consequent in consequents:
var_map = match(consequent, hypothesis)
if var_map is None:
continue
no_match = False
and_sub_condition = []
if var_map == {}:
and_sub_condition.append(AND())
conditions = rule.antecedent()
is_and = True
if not isinstance(conditions, list):
conditions = [conditions]
if isinstance(conditions, OR):
is_and = False
for condition in conditions:
pop_res = populate(condition, var_map)
and_sub_condition.append(backchain_to_goal_tree(rules, pop_res))
if is_and:
or_condition.append(AND(and_sub_condition))
else:
or_condition.append(OR(and_sub_condition))
if no_match:
return hypothesis
res = OR([hypothesis, OR(or_condition)])
res = simplify(res)
return res
def backchain_to_goal_tree(rules, hypothesis):
"""
Takes a hypothesis (string) and a list of rules (list
of IF objects), returning an AND/OR tree representing the
backchain of possible statements we may need to test
to determine if this hypothesis is reachable or not.
This method should return an AND/OR tree, that is, an
AND or OR object, whose constituents are the subgoals that
need to be tested. The leaves of this tree should be strings
(possibly with unbound variables), *not* AND or OR objects.
Make sure to use simplify(...) to flatten trees where appropriate.
"""
or_content = [hypothesis]
for rule in rules:
for consequent in rule.consequent():
_match = match(consequent, hypothesis)
if _match is not None:
or_content.append(recursive(rules, populate(rule.antecedent(),_match)))
return simplify(OR(or_content))
def backchain_to_goal_tree(rules, hypothesis):
temp = hypothesis
l = []
for i in rules:
q = i.consequent()
p = False
for j in q:
m = match(j,hypothesis)
if (m != None):
n = populate(i.antecedent(), m)
l.append(n)
if (len(l) == 0):
return hypothesis
for i in l:
if isinstance(i,str):
i = backchain_to_goal_tree(rules,i)
else:
x = len(i)
for j in range(x):
i[j] = backchain_to_goal_tree(rules,i[j])
temp = simplify(OR(temp, i))
return temp