def graphPlan(self):
"""
The graphplan algorithm.
The code calls the extract function which you should complete below
"""
#initialization
initState = self.initialState
level = 0
self.noGoods = [] #make sure you update noGoods in your backward search!
self.noGoods.append([])
#create first layer of the graph, note it only has a proposition layer which consists of the initial state.
propLayerInit = PropositionLayer()
for prop in initState:
propLayerInit.addProposition(prop)
pgInit = PlanGraphLevel()
pgInit.setPropositionLayer(propLayerInit)
self.graph.append(pgInit)
"""
While the layer does not contain all of the propositions in the goal state,
or some of these propositions are mutex in the layer we,
and we have not reached the fixed point, continue expanding the graph
"""
while self.goalStateNotInPropLayer(self.graph[level].getPropositionLayer().getPropositions()) or \
self.goalStateHasMutex(self.graph[level].getPropositionLayer()):
if self.isFixed(level):
return None #this means we stopped the while loop above because we reached a fixed point in the graph. nothing more to do, we failed!
self.noGoods.append([])
level = level + 1
pgNext = PlanGraphLevel() #create new PlanGraph object
pgNext.expand(self.graph[level - 1]) #calls the expand function, which you are implementing in the PlanGraph class
self.graph.append(pgNext) #appending the new level to the plan graph
sizeNoGood = len(self.noGoods[level]) #remember size of nogood table
plan = self.extract(self.graph, self.goal, level) #try to extract a plan since all of the goal propositions are in current graph level, and are not mutex
while(plan is None): #while we didn't extract a plan successfully
level = level + 1
self.noGoods.append([])
pgNext = PlanGraphLevel() #create next level of the graph by expanding
pgNext.expand(self.graph[level - 1]) #create next level of the graph by expanding
self.graph.append(pgNext)
plan = self.extract(self.graph, self.goal, level) #try to extract a plan again
if (plan is None and self.isFixed(level)): #if failed and reached fixed point
if sizeNoGood == len(self.noGoods[level]): #if size of nogood didn't change, means there's nothing more to do. We failed.
return None
sizeNoGood = len(self.noGoods[level]) #we didn't fail yet! update size of no good
return plan
def graphPlan(self):
"""
The graphplan algorithm.
The code calls the extract function which you should complete below
"""
#initialization
initState = self.initialState
level = 0
self.noGoods = [] #make sure you update noGoods in your backward search!
self.noGoods.append([])
#create first layer of the graph, note it only has a proposition layer which consists of the initial state.
propLayerInit = PropositionLayer()
for prop in initState:
propLayerInit.addProposition(prop)
pgInit = PlanGraphLevel()
pgInit.setPropositionLayer(propLayerInit)
self.graph.append(pgInit)
"""
While the layer does not contain all of the propositions in the goal state,
or some of these propositions are mutex in the layer we,
and we have not reached the fixed point, continue expanding the graph
"""
while self.goalStateNotInPropLayer(self.graph[level].getPropositionLayer().getPropositions()) or \
self.goalStateHasMutex(self.graph[level].getPropositionLayer()):
if self.isFixed(level):
return None #this means we stopped the while loop above because we reached a fixed point in the graph. nothing more to do, we failed!
self.noGoods.append([])
level = level + 1
pgNext = PlanGraphLevel() #create new PlanGraph object
pgNext.expand(self.graph[level - 1]) #calls the expand function, which you are implementing in the PlanGraph class
self.graph.append(pgNext) #appending the new level to the plan graph
sizeNoGood = len(self.noGoods[level]) #remember size of nogood table
plan = self.extract(self.graph, self.goal, level) #try to extract a plan since all of the goal propositions are in current graph level, and are not mutex
while(plan is None): #while we didn't extract a plan successfully
level = level + 1
self.noGoods.append([])
pgNext = PlanGraphLevel() #create next level of the graph by expanding
pgNext.expand(self.graph[level - 1]) #create next level of the graph by expanding
self.graph.append(pgNext)
plan = self.extract(self.graph, self.goal, level) #try to extract a plan again
if (plan is None and self.isFixed(level)): #if failed and reached fixed point
if sizeNoGood == len(self.noGoods[level]): #if size of nogood didn't change, means there's nothing more to do. We failed.
return None
sizeNoGood = len(self.noGoods[level]) #we didn't fail yet! update size of no good
return plan
def graphPlan(self): #El algoritmo graphplan en sí #Inicialización initState = self.initialState level = 0 self.noGoods = [] self.noGoods.append([]) #Crea la primera capa del grafo, que no consiste más que en el estado inicial propLayerInit = PropositionLayer() for prop in initState: propLayerInit.addProposition(prop) pgInit = PlanGraphLevel() pgInit.setPropositionLayer(propLayerInit) self.graph.append(pgInit) #Mientras que la capa no contiene todos los estados del estado final buscado (o están mutex) continuamos expandiendo el grafo while self.goalStateNotInPropLayer(self.graph[level].getPropositionLayer().getPropositions()) or \ self.goalStateHasMutex(self.graph[level].getPropositionLayer()): if self.isFixed(level): return None #Si llegamos aquí paramos porque significa que hemos llegado a un fixed point en el grafo, así que no podemos hacer nada más self.noGoods.append([]) level = level + 1 #Actualizamos el nivel pgNext = PlanGraphLevel() #Crea un nuevo objeto GraphPlan pgNext.expand(self.graph[level - 1]) #Llama a la función expandir self.graph.append(pgNext) #Une el nuevo nivel generado con el graphplan sizeNoGood = len(self.noGoods[level]) plan = self.extract(self.graph, self.goal, level) #Intentamos hallar un plan (si todos los estados objetivos están en este nivel y no están mutex) while(plan is None): #Hacemos esto mientras no podemos encontrar un plan level = level + 1 self.noGoods.append([]) pgNext = PlanGraphLevel() #Crea el próximo nivel del grafo pgNext.expand(self.graph[level - 1]) #Y ahora lo expande self.graph.append(pgNext) plan = self.extract(self.graph, self.goal, level) #Intentamos econtrar el plan if (plan is None and self.isFixed(level)): #Si fallamos y encontramos un punto un fixed point if sizeNoGood == len(self.noGoods[level]): #Si el tamaño de noGood no cambia significa que hemos fallado y no hay plan return None sizeNoGood = len(self.noGoods[level]) #Si no, significa que aún podemos encontrar el plan y actualizamos el tamaño de noGood return plan
