def maxLevel(state, problem):
"""
The heuristic value is the number of layers required to expand all goal propositions.
If the goal is not reachable from the state your heuristic should return float('inf')
A good place to start would be:
propLayerInit = PropositionLayer() #create a new proposition layer
for prop in state:
propLayerInit.addProposition(prop) #update the proposition layer with the propositions of the state
pgInit = PlanGraphLevel() #create a new plan graph level (level is the action layer and the propositions layer)
pgInit.setPropositionLayer(propLayerInit) #update the new plan graph level with the the proposition layer
"""
level = 0
graph = []
initial_prop_layer = PropositionLayer()
for prop in state:
initial_prop_layer.addProposition(prop)
curr_graph_level = PlanGraphLevel()
curr_graph_level.setPropositionLayer(initial_prop_layer)
graph.append(curr_graph_level)
while not problem.isGoalState(
graph[level].getPropositionLayer().getPropositions()):
if isFixed(graph, level):
return float('inf')
level += 1
next_level = PlanGraphLevel()
next_level.expandWithoutMutex(graph[level - 1])
graph.append(next_level)
return level
def levelSum(state, problem):
"""
The heuristic value is the sum of sub-goals level they first appeared.
If the goal is not reachable from the state your heuristic should return float('inf')
"""
level = 0
sum = 0
graph = []
goals = [goal for goal in problem.goal]
initial_prop_layer = PropositionLayer()
for prop in state:
initial_prop_layer.addProposition(prop)
initial_level = PlanGraphLevel()
initial_level.setPropositionLayer(initial_prop_layer)
graph.append(initial_level)
while len(goals) > 0:
if isFixed(graph, level):
return float('inf')
for goal in goals:
if goal in graph[level].getPropositionLayer().getPropositions():
sum += level
goals.remove(goal)
level += 1
next_level = PlanGraphLevel()
next_level.expandWithoutMutex(graph[level - 1])
graph.append(next_level)
return sum
def maxLevel(state, problem):
"""
The heuristic value is the number of layers required to expand all goal propositions.
If the goal is not reachable from the state your heuristic should return float('inf')
A good place to start would be:
propLayerInit = PropositionLayer() #create a new proposition layer
for prop in state:
propLayerInit.addProposition(prop) #update the proposition layer with the propositions of the state
pgInit = PlanGraphLevel() #create a new plan graph level (level is the action layer and the propositions layer)
pgInit.setPropositionLayer(propLayerInit) #update the new plan graph level with the the proposition layer
"""
"*** YOUR CODE HERE ***"
propLayerInit = PropositionLayer()
for prop in state:
propLayerInit.addProposition(prop)
pgInit = PlanGraphLevel()
pgInit.setPropositionLayer(propLayerInit)
level = 0
graph = []
graph.append(pgInit)
while not problem.isGoalState(
graph[level].getPropositionLayer().getPropositions()):
level += 1
pgNext = PlanGraphLevel()
pgNext.expandWithoutMutex(graph[level - 1])
graph.append(pgNext)
return level
def levelSum(state, problem):
"""
The heuristic value is the sum of sub-goals level they first appeared.
If the goal is not reachable from the state your heuristic should return float('inf')
"""
"*** YOUR CODE HERE ***"
propLayerInit = PropositionLayer()
for prop in state:
propLayerInit.addProposition(prop)
pgInit = PlanGraphLevel()
pgInit.setPropositionLayer(propLayerInit)
level = 0
sum = 0
graph = []
goals = [goal for goal in problem.goal]
graph.append(pgInit)
while len(goals) > 0:
for goal in goals:
if goal in graph[level].getPropositionLayer().getPropositions():
sum += level
goals.remove(goal)
level += 1
pgNext = PlanGraphLevel()
pgNext.expandWithoutMutex(graph[level - 1])
graph.append(pgNext)
return sum
def maxLevel(state, problem):
"""
The heuristic value is the number of layers required to expand all goal propositions.
If the goal is not reachable from the state your heuristic should return float('inf')
"""
level = 0
propLayerInit = PropositionLayer()
# Add all propositions in current state to proposition layer
for p in state:
propLayerInit.addProposition(p)
pgInit = PlanGraphLevel()
pgInit.setPropositionLayer(propLayerInit)
# Graph is a list of PlanGraphLevel objects
graph = []
graph.append(pgInit)
# While goal state is not in proposition layer, keep expanding
while problem.goalStateNotInPropLayer(graph[level].getPropositionLayer().getPropositions()):
# If the graph has not changed between expansions, we should halt
if isFixed(graph, level):
return float('inf')
level += 1
pgNext = PlanGraphLevel()
# Expand without mutex (relaxed version of problem)
pgNext.expandWithoutMutex(graph[level-1])
graph.append(pgNext)
return level
def maxLevel(state, problem):
"""
The heuristic value is the number of layers required to expand all goal propositions.
If the goal is not reachable from the state your heuristic should return float('inf')
A good place to start would be:
propLayerInit = PropositionLayer() #create a new proposition layer
for prop in state:
propLayerInit.addProposition(prop) #update the proposition layer with the propositions of the state
pgInit = PlanGraphLevel() #create a new plan graph level (level is the action layer and the propositions layer)
pgInit.setPropositionLayer(propLayerInit) #update the new plan graph level with the the proposition layer
"""
propLayerInit = PropositionLayer()
for prop in state:
propLayerInit.addProposition(prop)
pgInit = PlanGraphLevel()
pgInit.setPropositionLayer(propLayerInit)
graph = [] # list of PlanGraphLevel objects
graph.append(pgInit)
level = 0
while True:
# check if this level has the goal
if problem.goalStateNotInPropLayer(graph[level].getPropositionLayer().getPropositions()):
break
# else if the goal is not in this level, and we finished to max graph, meens we vant reach the goal.
elif isFixed(graph, level):
return float('inf')
pgNext = PlanGraphLevel()
pgNext.expandWithoutMutex(graph[level])
graph.append(pgNext)
level += 1
# if we got into break meens last level contain goal. So lets return the level.
return level
def levelSum(state, problem):
"""
The heuristic value is the sum of sub-goals level they first appeared.
If the goal is not reachable from the state your heuristic should return float('inf')
"""
propLayerInit = PropositionLayer() #create a new proposition layer
for prop in state:
propLayerInit.addProposition(prop) #update the proposition layer with the propositions of the state
pgInit = PlanGraphLevel() #create a new plan graph level (level is the action layer and the propositions layer)
pgInit.setPropositionLayer(propLayerInit) #update the new plan graph level with the the proposition layer
level = 0
sumLevel = 0
currentGoals = set(copy.copy(problem.goal))
while currentGoals: #TODO: Changed: run until all goals found/no solution possible
#check for new goals achieved
goalsInHand = set(pgInit.getPropositionLayer().getPropositions()) & currentGoals
if goalsInHand:
sumLevel += len(goalsInHand) * level;
currentGoals -= goalsInHand;
level += 1
## Expand to the next leyer
prevLayerSize = len(pgInit.getPropositionLayer().getPropositions())
pgInit.expandWithoutMutex(pgInit)
## Check if the expanded leyer is the same leyer as before
if len(pgInit.getPropositionLayer().getPropositions()) == prevLayerSize:
return float("inf")
return sumLevel
def maxLevel(state, problem):
"""
The heuristic value is the number of layers required to expand all goal propositions.
If the goal is not reachable from the state your heuristic should return float('inf')
"""
level = 0
propLayerInit = PropositionLayer()
# Add all propositions in current state to proposition layer
for p in state:
propLayerInit.addProposition(p)
pgInit = PlanGraphLevel()
pgInit.setPropositionLayer(propLayerInit)
# Graph is a list of PlanGraphLevel objects
graph = []
graph.append(pgInit)
# While goal state is not in proposition layer, keep expanding
while problem.goalStateNotInPropLayer(
graph[level].getPropositionLayer().getPropositions()):
# If the graph has not changed between expansions, we should halt
if isFixed(graph, level):
return float('inf')
level += 1
pgNext = PlanGraphLevel()
# Expand without mutex (relaxed version of problem)
pgNext.expandWithoutMutex(graph[level - 1])
graph.append(pgNext)
return level
def maxLevel(state, problem):
"""
The heuristic value is the number of layers required to expand all goal propositions.
If the goal is not reachable from the state your heuristic should return float('inf')
A good place to start would be:
propLayerInit = PropositionLayer() #create a new proposition layer
for prop in state:
propLayerInit.addProposition(prop) #update the proposition layer with the propositions of the state
pgInit = PlanGraphLevel() #create a new plan graph level (level is the action layer and the propositions layer)
pgInit.setPropositionLayer(propLayerInit) #update the new plan graph level with the the proposition layer
"""
level = 0
graph = []
initial_prop_layer = PropositionLayer()
for prop in state:
initial_prop_layer.addProposition(prop)
curr_graph_level = PlanGraphLevel()
curr_graph_level.setPropositionLayer(initial_prop_layer)
graph.append(curr_graph_level)
while not problem.isGoalState(graph[level].getPropositionLayer().getPropositions()):
if isFixed(graph, level):
return float('inf')
level += 1
next_level = PlanGraphLevel()
next_level.expandWithoutMutex(graph[level-1])
graph.append(next_level)
return level
def maxLevel(state, problem):
"""
The heuristic value is the number of layers required to expand all goal propositions.
If the goal is not reachable from the state your heuristic should return float('inf')
A good place to start would be:
propLayerInit = PropositionLayer() #create a new proposition layer
for prop in state:
propLayerInit.addProposition(prop) #update the proposition layer with the propositions of the state
pgInit = PlanGraphLevel() #create a new plan graph level (level is the action layer and the propositions layer)
pgInit.setPropositionLayer(propLayerInit) #update the new plan graph level with the the proposition layer
"""
""" this is copy paste from graph plan algorithm, with small change in loop definition and disabling of mutex """
propLayerInit = PropositionLayer() # create a new proposition layer
for prop in state:
propLayerInit.addProposition(prop) # update the proposition layer with the propositions of the state
pgInit = PlanGraphLevel() # create a new plan graph level (level is the action layer and the propositions layer)
pgInit.setPropositionLayer(propLayerInit)
graph = []
level = 0
graph.append(pgInit)
while problem.goalStateNotInPropLayer(graph[level].getPropositionLayer().getPropositions()):
if isFixed(graph, level):
return float("inf") # this means we stopped the while loop above because we reached a fixed point in the graph. nothing more to do, we failed!
level = level + 1
pgNext = PlanGraphLevel() # create new PlanGraph object
pgNext.expandWithoutMutex(graph[level - 1]) # calls the expand function, which you are implementing in the PlanGraph class
graph.append(pgNext) # appending the new level to the plan graph
return level
def levelSum(state, problem):
"""
The heuristic value is the sum of sub-goals level they first appeared.
If the goal is not reachable from the state your heuristic should return float('inf')
"""
""" this is copy paste from graph plan algorithm, with small change in loop definition and disabling of mutex """
propLayerInit = PropositionLayer() # create a new proposition layer
for prop in state:
propLayerInit.addProposition(prop) # update the proposition layer with the propositions of the state
pgInit = PlanGraphLevel() # create a new plan graph level (level is the action layer and the propositions layer)
pgInit.setPropositionLayer(propLayerInit)
graph = []
sum_sub_goals = 0
level = 0
graph.append(pgInit)
while problem.goalStateNotInPropLayer(graph[level].getPropositionLayer().getPropositions()):
if isFixed(graph, level):
return float("inf") # this means we stopped the while loop above because we reached a fixed point in the graph. nothing more to do, we failed!
if problem.isSubGoal(graph[level].getPropositionLayer().getPropositions()): # if we have sub goal here. count it
sum_sub_goals += 1
level += 1
pgNext = PlanGraphLevel() # create new PlanGraph object
pgNext.expandWithoutMutex(graph[level - 1]) # calls the expand function, which you are implementing in the PlanGraph class
graph.append(pgNext) # appending the new level to the plan graph
sum_sub_goals += 1 # the latest full sub goal that is equals goal, we take it to attention too
return sum_sub_goals
def levelSum(state, problem):
"""
The heuristic value is the sum of sub-goals level they first appeared.
If the goal is not reachable from the state your heuristic should return float('inf')
"""
level = 0
sum = 0
graph = []
goals = [goal for goal in problem.goal]
initial_prop_layer = PropositionLayer()
for prop in state:
initial_prop_layer.addProposition(prop)
initial_level = PlanGraphLevel()
initial_level.setPropositionLayer(initial_prop_layer)
graph.append(initial_level)
while len(goals) > 0:
if isFixed(graph, level):
return float('inf')
for goal in goals:
if goal in graph[level].getPropositionLayer().getPropositions():
sum += level
goals.remove(goal)
level += 1
next_level = PlanGraphLevel()
next_level.expandWithoutMutex(graph[level-1])
graph.append(next_level)
return sum
def levelSum(state, problem):
"""
The heuristic value is the sum of sub-goals level they first appeared.
If the goal is not reachable from the state your heuristic should return float('inf')
"""
"*** YOUR CODE HERE ***"
propLayerInit = PropositionLayer() #create a new proposition layer
# initialize the propositions
for prop in state:
propLayerInit.addProposition(prop) #update the proposition layer with the propositions of the state
pgInit = PlanGraphLevel() #create a new plan graph level (level is the action layer and the propositions layer)
pgInit.setPropositionLayer(propLayerInit) #update the new plan graph level with the the proposition layer
currentLevel = pgInit
level = 0
sum = 0
graph = list()
graph.append(currentLevel)
goals = copy.deepcopy(problem.goal)
while(len(goals) > 0):
if isFixed(graph, level):
return float("inf")
layer = currentLevel.getPropositionLayer().getPropositions()
for prop in layer:
if prop in goals:
sum = sum + level
goals.remove(prop)
nextLevel = PlanGraphLevel()
nextLevel.expandWithoutMutex(graph[level])
level = level + 1
graph.append(nextLevel)
currentLevel = nextLevel
return sum
def maxLevel(state, problem):
"""
The heuristic value is the number of layers required to expand all goal propositions.
If the goal is not reachable from the state your heuristic should return float('inf')
A good place to start would be:
propLayerInit = PropositionLayer() #create a new proposition layer
for prop in state:
propLayerInit.addProposition(prop) #update the proposition layer with the propositions of the state
pgInit = PlanGraphLevel() #create a new plan graph level (level is the action layer and the propositions layer)
pgInit.setPropositionLayer(propLayerInit) #update the new plan graph level with the the proposition layer
"""
"*** YOUR CODE HERE ***"
propLayerInit = PropositionLayer() #create a new proposition layer
for prop in state:
propLayerInit.addProposition(prop) #update the proposition layer with the propositions of the state
pgInit = PlanGraphLevel() #create a new plan graph level (level is the action layer and the propositions layer)
pgInit.setPropositionLayer(propLayerInit) #update the new plan graph level with the the proposition layer
currentLevel = pgInit
level = 0
graph = list()
graph.append(currentLevel)
while(problem.goalStateNotInPropLayer(graph[level].getPropositionLayer().getPropositions())):
newLevel = PlanGraphLevel()
newLevel.expandWithoutMutex(graph[level])
level += 1
graph.append(newLevel)
currentLevel = newLevel
if isFixed(graph, level):
return float("inf")
return level
def maxLevel(state, problem):
""" El valor de la heurística es el número de capas
necesarias para expandir todas las proposiciones de gol.
Si el objetivo no es alcanzable desde el estado de su
heurística debe volver float('inf') """
level = 0
propLayerInit = PropositionLayer()
# Añadir todas las proposiciones en el estado actual en la propositionLayer
for p in state:
propLayerInit.addProposition(p)
pgInit = PlanGraphLevel()
pgInit.setPropositionLayer(propLayerInit)
# El Grafo es una lista de objetos PlanGraphLevel
graph = []
graph.append(pgInit)
# Mientras que el estado objetivo no está en la capa proposición, seguimos expandiendolo
while problem.goalStateNotInPropLayer(graph[level].getPropositionLayer().getPropositions()):
# Si el grafo no ha cambiado entre expansiones, lo detenemos.
if isFixed(graph, level):
return float('inf')
level += 1
pgNext = PlanGraphLevel()
# Expandir sin mutex (versión relajada de problema)
pgNext.expandWithoutMutex(graph[level-1])
graph.append(pgNext)
return level
def maxLevel(state, problem):
"""
The heuristic value is the number of layers required to expand all goal propositions.
If the goal is not reachable from the state your heuristic should return float('inf')
A good place to start would be:
propLayerInit = PropositionLayer() #create a new proposition layer
for prop in state:
propLayerInit.addProposition(prop) #update the proposition layer with the propositions of the state
pgInit = PlanGraphLevel() #create a new plan graph level (level is the action layer and the propositions layer)
pgInit.setPropositionLayer(propLayerInit) #update the new plan graph level with the the proposition layer
"""
"*** YOUR CODE HERE ***"
propLayerInit = PropositionLayer() #create a new proposition layer
for prop in state:
propLayerInit.addProposition(prop) #update the proposition layer with the propositions of the state
pgInit = PlanGraphLevel() #create a new plan graph level (level is the action layer and the propositions layer)
pgInit.setPropositionLayer(propLayerInit) #update the new plan graph level with the the proposition layer
level = 0;
while not problem.isGoalState(pgInit.getPropositionLayer().getPropositions()):
level += 1
## Expand to the next leyer
prevLayerSize = len(pgInit.getPropositionLayer().getPropositions())
pgInit.expandWithoutMutex(pgInit)
## Check if the expanded leyer is the same leyer as before
if len(pgInit.getPropositionLayer().getPropositions()) == prevLayerSize:
return float("inf")
return level
def levelSum(state, problem):
"""
The heuristic value is the sum of sub-goals level they first appeared.
If the goal is not reachable from the state your heuristic should return float('inf')
"""
total = 0
propLayerInit = PropositionLayer()
for prop in state:
propLayerInit.addProposition(prop)
pgInit = PlanGraphLevel()
pgInit.setPropositionLayer(propLayerInit)
g = [pgInit]
level = 0
while len(problem.goal) > 0:
if isFixed(g, level):
return float("inf")
for goal in problem.goal:
if goal in g[level].getPropositionLayer().getPropositions():
problem.goal.remove(goal)
total += level
nextPlanGraphLevel = PlanGraphLevel()
nextPlanGraphLevel.expandWithoutMutex(g[level])
level += 1
g.append(nextPlanGraphLevel)
return total
def getStartState(self):
"*** YOUR CODE HERE ***"
# explain: A state is a planGraphLevel,
# and here I am building a level with the initial state propositions
propLayerInit = PropositionLayer()
for prop in self.initialState:
propLayerInit.addProposition(prop)
pgInit = PlanGraphLevel()
pgInit.setPropositionLayer(propLayerInit)
return pgInit
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
def expansionGenerator(state, problem):
"""
Generates and yields the propositions in each level,
Until the graph becomes fixed.
"""
propLayerInit = PropositionLayer() #create a new proposition layer
for prop in state:
propLayerInit.addProposition(prop) #update the proposition layer with the propositions of the state
pgInit = PlanGraphLevel() #create a new plan graph level (level is the action layer and the propositions layer)
pgInit.setPropositionLayer(propLayerInit) #update the new plan graph level with the the proposition layer
graph = [pgInit]
count = 0
while not isFixed(graph, count):
props = graph[count].getPropositionLayer().getPropositions()
yield count, props
pgNext = PlanGraphLevel()
pgNext.expandWithoutMutex(graph[count])
graph.append(pgNext)
count += 1
def levelSum(state, problem):
"""
The heuristic value is the sum of sub-goals level they first appeared.
If the goal is not reachable from the state your heuristic should return float('inf')
"""
propLayerInit = PropositionLayer()
for p in state:
propLayerInit.addProposition(p)
pgInit = PlanGraphLevel()
pgInit.setPropositionLayer(propLayerInit)
graph = [] # list of PlanGraphLevel objects
graph.append(pgInit)
goals = problem.goal[:]
level = 0
sum_ = 0
# keep expanding as long as we still have goal states we didn't see
while goals:
if isFixed(graph, level):
# if the graph is fixed and expansions didn't change in the last level, it means that we can't reach
# the goal state, and we return infinity
return float('inf')
props = graph[level].getPropositionLayer().getPropositions()
for goal in goals:
if goal in props:
# each goal state that we run into, we should add to the sum, and remove it from the goals we need to see
sum_ += level
goals.remove(goal)
pg = PlanGraphLevel()
# expanding using a easier version of the problem - without mutexes
pg.expandWithoutMutex(graph[level])
graph.append(pg)
level += 1
sum_ += level
return sum_
def maxLevel(state, problem):
"""
The heuristic value is the number of layers required to expand all goal propositions.
If the goal is not reachable from the state your heuristic should return float('inf')
A good place to start would be:
propLayerInit = PropositionLayer() #create a new proposition layer
for prop in state:
propLayerInit.addProposition(prop) #update the proposition layer with the propositions of the state
pgInit = PlanGraphLevel() #create a new plan graph level (level is the action layer and the propositions layer)
pgInit.setPropositionLayer(propLayerInit) #update the new plan graph level with the the proposition layer
"""
propLayerInit = PropositionLayer()
for p in state:
propLayerInit.addProposition(p)
pgInit = PlanGraphLevel()
pgInit.setPropositionLayer(propLayerInit)
graph = [] # list of PlanGraphLevel objects
graph.append(pgInit)
level = 0
# keep expanding as long as we don't hit the goal state
while problem.goalStateNotInPropLayer(
graph[level].getPropositionLayer().getPropositions()):
if isFixed(graph, level):
# if the graph is fixed and expansions didn't change in the last level, it means that we can't reach
# the goal state, and we return infinity
return float('inf')
pg = PlanGraphLevel()
# expanding using a easier version of the problem - without mutexes
pg.expandWithoutMutex(graph[level])
graph.append(pg)
level += 1
return level
def levelSum(state, problem):
"""
The heuristic value is the sum of sub-goals level they first appeared.
If the goal is not reachable from the state your heuristic should return float('inf')
"""
propLayerInit = PropositionLayer()
for prop in state:
propLayerInit.addProposition(prop)
pgInit = PlanGraphLevel()
pgInit.setPropositionLayer(propLayerInit)
graph = [] # list of PlanGraphLevel objects
graph.append(pgInit)
level = 0
leftGoals = problem.goal.copy()
level_sum = 0
while True:
# if leftGoals is empty, means we reached all the goals.
if len(leftGoals) == 0:
break
# else if the goal is not in this level, and we finished to max graph, meens we vant reach the goal.
elif isFixed(graph, level):
return float('inf')
props = graph[level].getPropositionLayer().getPropositions()
# check for each goal if it is in the next props. If so, remove it from the left golas, and add the level to the sum
for goal in leftGoals:
if goal in props:
level_sum += level
leftGoals.remove(goal)
pgTemp = PlanGraphLevel()
pgTemp.expandWithoutMutex(graph[level])
graph.append(pgTemp)
level += 1
# adding last level to the sum, and return it
level_sum += level
return level_sum
def maxLevel(state, problem):
"""
The heuristic value is the number of layers required to expand all goal propositions.
If the goal is not reachable from the state your heuristic should return float('inf')
"""
newPropositionLayer = PropositionLayer()
[newPropositionLayer.addProposition(p) for p in state]
newPlanGraphLevel = PlanGraphLevel()
newPlanGraphLevel.setPropositionLayer(newPropositionLayer)
level = 0
g = [newPlanGraphLevel]
while problem.goalStateNotInPropLayer(g[level].getPropositionLayer().getPropositions()):
if isFixed(g, level):
return float("inf")
level += 1
nextPlanGraphLevel = PlanGraphLevel()
nextPlanGraphLevel.expandWithoutMutex(g[level - 1])
g.append(newPlanGraphLevel)
return level
def levelSum(state, problem):
"""
The heuristic value is the sum of sub-goals level they first appeared.
If the goal is not reachable from the state your heuristic should return float('inf')
"""
def nextPlan(plan):
next_plan = PlanGraphLevel()
next_plan.expandWithoutMutex(plan)
return next_plan, next_plan.getPropositionLayer().getPropositions()
propLayerInit = PropositionLayer()
# add all to the new proposition layer
lmap(propLayerInit.addProposition, state)
plan = PlanGraphLevel()
plan.setPropositionLayer(propLayerInit)
plan_propositions = plan.getPropositionLayer().getPropositions()
# create a graph that will store all the plan levels
graph = []
graph.append(plan)
goals_levels = dict()
goal = problem.goal
# init goals levels
for p in goal:
goals_levels[p.getName()] = None
# as long as we have for one of the goal None we didnt find the first level
while None in goals_levels.values():
# if fixed we won't have a solution
if isFixed(graph, len(graph) - 1):
return float('inf')
# for each prop in the goal check if exist on the current plan
# propositions
for p in goal:
# check that we didnt assign a value yet
if p in plan_propositions and goals_levels[p.getName()] == None:
# set the current level as the fist appearance of the prop
goals_levels[p.getName()] = len(graph) - 1
# create the next plan by the prev
plan, plan_propositions = nextPlan(plan)
# store in the graph
graph.append(plan)
return sum(goals_levels.values())
def maxLevel(state, problem):
"""
The heuristic value is the number of layers required to expand all goal propositions.
If the goal is not reachable from the state your heuristic should return float('inf')
A good place to start would be:
propLayerInit = PropositionLayer() #create a new proposition layer
for prop in state:
#update the proposition layer with the propositions of the state
propLayerInit.addProposition(prop)
# create a new plan graph level (level is the action layer and the
# propositions layer)
pgInit = PlanGraphLevel()
#update the new plan graph level with the the proposition layer
pgInit.setPropositionLayer(propLayerInit)
"""
def nextPlan(plan):
next_plan = PlanGraphLevel()
next_plan.expandWithoutMutex(plan)
return next_plan, next_plan.getPropositionLayer().getPropositions()
propLayerInit = PropositionLayer()
# add all to the new proposition layer
lmap(propLayerInit.addProposition, state)
plan = PlanGraphLevel()
plan.setPropositionLayer(propLayerInit)
plan_propositions = plan.getPropositionLayer().getPropositions()
# create a graph that will store all the plan levels
graph = []
graph.append(plan)
# if we found we can rest
while not problem.isGoalState(plan_propositions):
# if fixed we won't have a solution
if isFixed(graph, len(graph) - 1):
return float('inf')
# create the next plan by the prev
plan, plan_propositions = nextPlan(plan)
# store in the graph
graph.append(plan)
return len(graph) - 1
class PlanGraphLevel(object):
"""
A class for representing a level in the plan graph.
For each level i, the PlanGraphLevel consists of the actionLayer and propositionLayer at this level in this order!
"""
independentActions = [] # updated to the independentActions of the propblem GraphPlan.py line 31
actions = [] # updated to the actions of the problem GraphPlan.py line 32 and planningProblem.py line 25
props = [] # updated to the propositions of the problem GraphPlan.py line 33 and planningProblem.py line 26
@staticmethod
def setIndependentActions(independentActions):
PlanGraphLevel.independentActions = independentActions
@staticmethod
def setActions(actions):
PlanGraphLevel.actions = actions
@staticmethod
def setProps(props):
PlanGraphLevel.props = props
def __init__(self):
"""
Constructor
"""
self.actionLayer = ActionLayer() # see actionLayer.py
self.propositionLayer = PropositionLayer() # see propositionLayer.py
def getPropositionLayer(self):
return self.propositionLayer
def setPropositionLayer(self, propLayer):
self.propositionLayer = propLayer
def getActionLayer(self):
return self.actionLayer
def setActionLayer(self, actionLayer):
self.actionLayer = actionLayer
def updateActionLayer(self, previousPropositionLayer):
"""
Updates the action layer given the previous proposition layer (see propositionLayer.py)
allAction is the list of all the actions (include noOp in the domain)
"""
allActions = PlanGraphLevel.actions
for a in allActions:
if previousPropositionLayer.allPrecondsInLayer(a):
self.actionLayer.addAction(a)
def updateMutexActions(self, previousLayerMutexProposition):
"""
Updates the mutex list in self.actionLayer,
given the mutex proposition from the previous layer.
currentLayerActions are the actions in the current action layer
"""
currentLayerActions = self.actionLayer.getActions()
for a_i in currentLayerActions:
for a_j in currentLayerActions:
if a_i != a_j and mutexActions(a_i, a_j, previousLayerMutexProposition):
if Pair(a_i,a_j) not in self.actionLayer.mutexActions:
self.actionLayer.addMutexActions(a_i,a_j)
def updatePropositionLayer(self):
"""
Updates the propositions in the current proposition layer,
given the current action layer.
don't forget to update the producers list!
"""
currentLayerActions = self.actionLayer.getActions()
for a in currentLayerActions:
for p in a.getAdd():
if p not in self.propositionLayer.getPropositions():
self.propositionLayer.addProposition(p)
p.addProducer(a)
def updateMutexProposition(self):
"""
updates the mutex propositions in the current proposition layer
"""
currentLayerPropositions = self.propositionLayer.getPropositions()
currentLayerMutexActions = self.actionLayer.getMutexActions()
for p_i in currentLayerPropositions:
for p_j in currentLayerPropositions:
if p_i != p_j and mutexPropositions(p_i,p_j,currentLayerMutexActions):
if Pair(p_i,p_j) not in self.propositionLayer.mutexPropositions:
self.propositionLayer.mutexPropositions.append(Pair(p_i,p_j))
def expand(self, previousLayer):
"""
Your algorithm should work as follows:
First, given the propositions and the list of mutex propositions from the previous layer,
set the actions in the action layer.
Then, set the mutex action in the action layer.
Finally, given all the actions in the current layer, set the propositions and their mutex relations in the proposition layer.
"""
previousPropositionLayer = previousLayer.getPropositionLayer()
previousLayerMutexProposition = previousPropositionLayer.getMutexProps()
self.updateActionLayer(previousPropositionLayer)
self.updateMutexActions(previousLayerMutexProposition)
self.updatePropositionLayer()
self.updateMutexProposition()
def expandWithoutMutex(self, previousLayer):
"""
Questions 11 and 12
You don't have to use this function
"""
previousLayerProposition = previousLayer.getPropositionLayer()
self.updateActionLayer(previousLayerProposition)
self.updatePropositionLayer()
class PlanGraphLevel(object):
"""
A class for representing a level in the plan graph.
For each level i, the PlanGraphLevel consists of the actionLayer and propositionLayer at this level in this order!
"""
independentActions = [
] # updated to the independentActions of the propblem GraphPlan.py line 31
actions = [
] # updated to the actions of the problem GraphPlan.py line 32 and planningProblem.py line 25
props = [
] # updated to the propositions of the problem GraphPlan.py line 33 and planningProblem.py line 26
@staticmethod
def setIndependentActions(independentActions):
PlanGraphLevel.independentActions = independentActions
@staticmethod
def setActions(actions):
PlanGraphLevel.actions = actions
@staticmethod
def setProps(props):
PlanGraphLevel.props = props
def __init__(self):
"""
Constructor
"""
self.actionLayer = ActionLayer() # see actionLayer.py
self.propositionLayer = PropositionLayer() # see propositionLayer.py
def getPropositionLayer(self):
# returns the proposition layer
return self.propositionLayer
def setPropositionLayer(self, propLayer):
# sets the proposition layer
self.propositionLayer = propLayer
def getActionLayer(self):
# returns the action layer
return self.actionLayer
def setActionLayer(self, actionLayer):
# sets the action layer
self.actionLayer = actionLayer
def updateActionLayer(self, previousPropositionLayer):
"""
Updates the action layer given the previous proposition layer (see propositionLayer.py)
You should add an action to the layer if its preconditions are in the previous propositions layer,
and the preconditions are not pairwise mutex.
allAction is the list of all the action (include noOp) in the domain
You might want to use those functions:
previousPropositionLayer.isMutex(prop1, prop2) returns true if prop1 and prop2 are mutex at the previous propositions layer
previousPropositionLayer.allPrecondsInLayer(action) returns true if all the preconditions of action are in the previous propositions layer
self.actionLayer.addAction(action) adds action to the current action layer
"""
allActions = PlanGraphLevel.actions
"*** YOUR CODE HERE ***"
for action in allActions:
if previousPropositionLayer.allPrecondsInLayer(action):
self.actionLayer.addAction(action)
def updateMutexActions(self, previousLayerMutexProposition):
"""
Updates the mutex list in self.actionLayer,
given the mutex proposition from the previous layer.
currentLayerActions are the actions in the current action layer
You might want to use this function:
self.actionLayer.addMutexActions(action1, action2)
adds the pair (action1, action2) to the mutex list in the current action layer
Note that action is *not* mutex with itself
"""
currentLayerActions = self.actionLayer.getActions()
"*** YOUR CODE HERE ***"
index = 0
for action1 in currentLayerActions:
index += 1
for action2 in currentLayerActions[index:]:
is_mutex = False
for pre1 in action1.getPre():
for pre2 in action2.getPre():
if Pair(pre1, pre2) in previousLayerMutexProposition:
is_mutex = True
break
if Pair(action1, action2) not in self.independentActions:
is_mutex = True
if is_mutex:
self.actionLayer.addMutexActions(action1, action2)
def updatePropositionLayer(self):
"""
Updates the propositions in the current proposition layer,
given the current action layer.
don't forget to update the producers list!
Note that same proposition in different layers might have different producers lists,
hence you should create two different instances.
currentLayerActions is the list of all the actions in the current layer.
You might want to use those functions:
dict() creates a new dictionary that might help to keep track on the propositions that you've
already added to the layer
self.propositionLayer.addProposition(prop) adds the proposition prop to the current layer
"""
currentLayerActions = self.actionLayer.getActions()
"*** YOUR CODE HERE ***"
Propositions = dict()
for action in currentLayerActions:
for prop in action.getAdd():
name = prop.getName()
if Propositions.has_key(name):
new_prop = Propositions[name]
else:
new_prop = Proposition(name)
Propositions[name] = new_prop
self.propositionLayer.addProposition(new_prop)
new_prop.addProducer(action)
def updateMutexProposition(self):
"""
updates the mutex propositions in the current proposition layer
You might want to use those functions:
mutexPropositions(prop1, prop2, currentLayerMutexActions) returns true if prop1 and prop2 are mutex in the current layer
self.propositionLayer.addMutexProp(prop1, prop2) adds the pair (prop1, prop2) to the mutex list of the current layer
"""
currentLayerPropositions = self.propositionLayer.getPropositions()
currentLayerMutexActions = self.actionLayer.getMutexActions()
"*** YOUR CODE HERE ***"
index = 0
for prop1 in currentLayerPropositions:
index += 1
for prop2 in currentLayerPropositions[index:]:
if mutexPropositions(prop1, prop2, currentLayerMutexActions):
self.propositionLayer.addMutexProp(prop1, prop2)
def expand(self, previousLayer):
"""
Your algorithm should work as follows:
First, given the propositions and the list of mutex propositions from the previous layer,
set the actions in the action layer.
Then, set the mutex action in the action layer.
Finally, given all the actions in the current layer,
set the propositions and their mutex relations in the proposition layer.
"""
previousPropositionLayer = previousLayer.getPropositionLayer()
previousLayerMutexProposition = previousPropositionLayer.getMutexProps(
)
"*** YOUR CODE HERE ***"
self.updateActionLayer(previousPropositionLayer)
self.updateMutexActions(previousLayerMutexProposition)
self.updatePropositionLayer()
self.updateMutexProposition()
#print('expand')
#print('actions ' + str([action.getName() for action in self.actionLayer.actions]))
#print('propositions ' + str([prop.getName() for prop in self.propositionLayer.propositions]))
#print('actions mutexes ' + str([pair.a.getName()+","+pair.b.getName() for pair in self.actionLayer.mutexActions]))
#print('propositions mutexes ' + str([pair.a.getName()+","+pair.b.getName() for pair in self.propositionLayer.mutexPropositions]))
def expandWithoutMutex(self, previousLayer):
"""
Questions 11 and 12
You don't have to use this function
"""
previousLayerProposition = previousLayer.getPropositionLayer()
"*** YOUR CODE HERE ***"
self.updateActionLayer(previousLayerProposition)
self.updatePropositionLayer()
class PlanGraphLevel(object):
"""
A class for representing a level in the plan graph.
For each level i, the PlanGraphLevel consists of the actionLayer and propositionLayer at this level in this order!
"""
independentActions = [] # updated to the independentActions of the propblem GraphPlan.py line 31
actions = [] # updated to the actions of the problem GraphPlan.py line 32 and planningProblem.py line 25
props = [] # updated to the propositions of the problem GraphPlan.py line 33 and planningProblem.py line 26
@staticmethod
def setIndependentActions(independentActions):
PlanGraphLevel.independentActions = independentActions
@staticmethod
def setActions(actions):
PlanGraphLevel.actions = actions
@staticmethod
def setProps(props):
PlanGraphLevel.props = props
def __init__(self):
"""
Constructor
"""
self.actionLayer = ActionLayer() # see actionLayer.py
self.propositionLayer = PropositionLayer() # see propositionLayer.py
def getPropositionLayer(self):
# returns the proposition layer
return self.propositionLayer
def setPropositionLayer(self, propLayer):
# sets the proposition layer
self.propositionLayer = propLayer
def getActionLayer(self):
# returns the action layer
return self.actionLayer
def setActionLayer(self, actionLayer):
# sets the action layer
self.actionLayer = actionLayer
def updateActionLayer(self, previousPropositionLayer):
"""
Updates the action layer given the previous proposition layer (see propositionLayer.py)
You should add an action to the layer if its preconditions are in the previous propositions layer,
and the preconditions are not pairwise mutex.
allAction is the list of all the action (include noOp) in the domain
You might want to use those functions:
previousPropositionLayer.isMutex(prop1, prop2) returns true if prop1 and prop2 are mutex at the previous propositions layer
previousPropositionLayer.allPrecondsInLayer(action) returns true if all the preconditions of action are in the previous propositions layer
self.actionLayer.addAction(action) adds action to the current action layer
"""
# Check all possible actions
for action in PlanGraphLevel.actions:
if previousPropositionLayer.allPrecondsInLayer(action): # only if all pre. exist, then try to add it
mutexes = previousPropositionLayer.getMutexProps()
has_mutexes = False
# check for all pairs of mutexes, also if we have one pre. only, it will work
for p1, p2 in product(action.getPre(), action.getPre()):
if Pair(p1, p2) in mutexes:
has_mutexes = True
break # mutex found, we don't want to continue our checking
if not has_mutexes:
self.actionLayer.addAction(action)
def updateMutexActions(self, previousLayerMutexProposition):
"""
Updates the mutex list in self.actionLayer,
given the mutex proposition from the previous layer.
currentLayerActions are the actions in the current action layer
You might want to use this function:
self.actionLayer.addMutexActions(action1, action2)
adds the pair (action1, action2) to the mutex list in the current action layer
Note that action is *not* mutex with itself
"""
current_layer_actions = self.actionLayer.getActions()
for a1, a2 in product(current_layer_actions, current_layer_actions):
if a1 != a2 and mutexActions(a1, a2, previousLayerMutexProposition):
self.actionLayer.addMutexActions(a1, a2)
def updatePropositionLayer(self):
"""
Updates the propositions in the current proposition layer,
given the current action layer.
don't forget to update the producers list!
Note that same proposition in different layers might have different producers lists,
hence you should create two different instances.
currentLayerActions is the list of all the actions in the current layer.
You might want to use those functions:
dict() creates a new dictionary that might help to keep track on the propositions that you've
already added to the layer
self.propositionLayer.addProposition(prop) adds the proposition prop to the current layer
"""
added_props = {} # this dictionary will see what propositions were added
# for all actions do the test
for action in self.actionLayer.getActions():
for prop in action.getAdd():
if prop.getName() not in added_props: # we don't wan't to add proposition twice
added_props[prop.getName()] = prop
self.getPropositionLayer().addProposition(prop)
else:
# in case action not in producers, not see that issue but was asked to add that line in description
if action not in prop.getProducers():
prop.addProducer(action)
def updateMutexProposition(self):
"""
updates the mutex propositions in the current proposition layer
You might want to use those functions:
mutexPropositions(prop1, prop2, currentLayerMutexActions) returns true if prop1 and prop2 are mutex in the current layer
self.propositionLayer.addMutexProp(prop1, prop2) adds the pair (prop1, prop2) to the mutex list of the current layer
"""
currentLayerPropositions = self.propositionLayer.getPropositions()
currentLayerMutexActions = self.actionLayer.getMutexActions()
for p1, p2 in product(currentLayerPropositions, currentLayerPropositions):
if p1 != p2 and mutexPropositions(p1, p2, currentLayerMutexActions):
self.getPropositionLayer().addMutexProp(p1, p2)
def expand(self, previousLayer):
"""
Your algorithm should work as follows:
First, given the propositions and the list of mutex propositions from the previous layer,
set the actions in the action layer.
Then, set the mutex action in the action layer.
Finally, given all the actions in the current layer,
set the propositions and their mutex relations in the proposition layer.
"""
previousPropositionLayer = previousLayer.getPropositionLayer()
previousLayerMutexProposition = previousPropositionLayer.getMutexProps()
self.updateActionLayer(previousPropositionLayer)
self.updateMutexActions(previousLayerMutexProposition)
self.updatePropositionLayer()
self.updateMutexProposition()
def expandWithoutMutex(self, previousLayer):
"""
Questions 11 and 12
You don't have to use this function
"""
previousLayerProposition = previousLayer.getPropositionLayer()
self.updateActionLayer(previousLayerProposition)
self.updatePropositionLayer()
class PlanGraphLevel(object):
"""
A class for representing a level in the plan graph.
For each level i, the PlanGraphLevel consists of the actionLayer and propositionLayer at this level in this order!
"""
independentActions = [
] # updated to the independentActions of the propblem GraphPlan.py line 31
# updated to the actions of the problem GraphPlan.py line 32 and
# planningProblem.py line 25
actions = []
# updated to the propositions of the problem GraphPlan.py line 33 and
# planningProblem.py line 26
props = []
@staticmethod
def setIndependentActions(independentActions):
PlanGraphLevel.independentActions = independentActions
@staticmethod
def setActions(actions):
PlanGraphLevel.actions = actions
@staticmethod
def setProps(props):
PlanGraphLevel.props = props
def __init__(self):
"""
Constructor
"""
self.actionLayer = ActionLayer() # see actionLayer.py
self.propositionLayer = PropositionLayer() # see propositionLayer.py
def getPropositionLayer(self):
# returns the proposition layer
return self.propositionLayer
def setPropositionLayer(self, propLayer):
# sets the proposition layer
self.propositionLayer = propLayer
def getActionLayer(self):
# returns the action layer
return self.actionLayer
def setActionLayer(self, actionLayer):
# sets the action layer
self.actionLayer = actionLayer
def updateActionLayer(self, previousPropositionLayer):
"""
Updates the action layer given the previous proposition layer (see propositionLayer.py)
You should add an action to the layer if its preconditions are in the previous propositions layer,
and the preconditions are not pairwise mutex.
allAction is the list of all the action (include noOp) in the domain
You might want to use those functions:
previousPropositionLayer.isMutex(prop1, prop2) returns true if prop1 and prop2 are mutex at the previous propositions layer
previousPropositionLayer.allPrecondsInLayer(action) returns true if all the preconditions of action are in the previous propositions layer
self.actionLayer.addAction(action) adds action to the current action layer
"""
allActions = PlanGraphLevel.actions
def isAllPrecondsInLayer(action):
return previousPropositionLayer.allPrecondsInLayer(action)
def isPropositionMutex(action):
# create all the combinations of the actions preconditions
all_combinations = [(cond1, cond2) for cond1 in action.getPre()
for cond2 in action.getPre()]
# check if exists at least one mutex
are_any_mutex = any(
lmap(
lambda conds: previousPropositionLayer.isMutex(
conds[0], conds[1]),
lfilter(isDifferent, all_combinations)))
# retun if rthere are no mutex
return not are_any_mutex
addActionToLayer = self.actionLayer.addAction
lmap(
addActionToLayer,
lfilter(isPropositionMutex,
lfilter(isAllPrecondsInLayer, allActions)))
def updateMutexActions(self, previousLayerMutexProposition):
"""
Updates the mutex list in self.actionLayer,
given the mutex proposition from the previous layer.
currentLayerActions are the actions in the current action layer
You might want to use this function:
self.actionLayer.addMutexActions(action1, action2)
adds the pair (action1, action2) to the mutex list in the current action layer
Note that action is *not* mutex with itself
"""
currentLayerActions = self.actionLayer.getActions()
# create all the combinations of the actions
all_combinations = [(action1, action2)
for action1 in currentLayerActions
for action2 in currentLayerActions]
def isMutexActions(actions):
a1, a2 = actions
return mutexActions(a1, a2, previousLayerMutexProposition)
def isNotIn(actions):
a1, a2 = actions
return Pair(a1, a2) not in self.actionLayer.mutexActions
def addMutexActions(actions):
a1, a2 = actions
self.actionLayer.addMutexActions(a1, a2)
lmap(
addMutexActions,
lfilter(
isNotIn,
lfilter(isMutexActions, lfilter(isDifferent,
all_combinations))))
def updatePropositionLayer(self):
"""
Updates the propositions in the current proposition layer,
given the current action layer.
don't forget to update the producers list!
Note that same proposition in different layers might have different producers lists,
hence you should create two different instances.
currentLayerActions is the list of all the actions in the current layer.
You might want to use those functions:
dict() creates a new dictionary that might help to keep track on the propositions that you've
already added to the layer
self.propositionLayer.addProposition(prop) adds the proposition prop to the current layer
"""
currentLayerActions = self.actionLayer.getActions()
def isNotIn(prop):
return prop not in self.propositionLayer.getPropositions()
addProposition = self.propositionLayer.addProposition
for action in currentLayerActions:
for prop in action.getAdd():
if isNotIn(prop):
addProposition(prop)
prop.addProducer(action)
def updateMutexProposition(self):
"""
updates the mutex propositions in the current proposition layer
You might want to use those functions:
mutexPropositions(prop1, prop2, currentLayerMutexActions) returns true if prop1 and prop2 are mutex in the current layer
self.propositionLayer.addMutexProp(prop1, prop2) adds the pair (prop1, prop2) to the mutex list of the current layer
"""
currentLayerPropositions = self.propositionLayer.getPropositions()
currentLayerMutexActions = self.actionLayer.getMutexActions()
# create all the combinations of the propositions
all_combinations = [(prop1, prop2)
for prop1 in currentLayerPropositions
for prop2 in currentLayerPropositions]
def isMutexPropositions(propositions):
p1, p2 = propositions
return mutexPropositions(p1, p2, currentLayerMutexActions)
def isNotIn(propositions):
p1, p2 = propositions
return Pair(p1, p2) not in self.propositionLayer.mutexPropositions
def addMutexPropositions(propositions):
p1, p2 = propositions
return self.propositionLayer.mutexPropositions.append(Pair(p1, p2))
lmap(
addMutexPropositions,
lfilter(
isNotIn,
lfilter(isMutexPropositions,
lfilter(isDifferent, all_combinations))))
def expand(self, previousLayer):
"""
Your algorithm should work as follows:
First, given the propositions and the list of mutex propositions from the previous layer,
set the actions in the action layer.
Then, set the mutex action in the action layer.
Finally, given all the actions in the current layer,
set the propositions and their mutex relations in the proposition layer.
"""
previousPropositionLayer = previousLayer.getPropositionLayer()
previousLayerMutexProposition = previousPropositionLayer.getMutexProps(
)
self.updateActionLayer(previousPropositionLayer)
self.updateMutexActions(previousLayerMutexProposition)
self.updatePropositionLayer()
self.updateMutexProposition()
def expandWithoutMutex(self, previousLayer):
"""
Questions 11 and 12
You don't have to use this function
"""
previousLayerProposition = previousLayer.getPropositionLayer()
self.updateActionLayer(previousLayerProposition)
self.updatePropositionLayer()
def __init__(self):
self.actionLayer = ActionLayer() # see actionLayer.py
self.propositionLayer = PropositionLayer() # see propositionLayer.py
class PlanGraphLevel(object):
"""
A class for representing a level in the plan graph.
For each level i, the PlanGraphLevel consists of the actionLayer and propositionLayer at this level in this order!
"""
independentActions = [] # updated to the independentActions of the propblem GraphPlan.py line 31
actions = [] # updated to the actions of the problem GraphPlan.py line 32 and planningProblem.py line 25
props = [] # updated to the propositions of the problem GraphPlan.py line 33 and planningProblem.py line 26
@staticmethod
def setIndependentActions(independentActions):
PlanGraphLevel.independentActions = independentActions
@staticmethod
def setActions(actions):
PlanGraphLevel.actions = actions
@staticmethod
def setProps(props):
PlanGraphLevel.props = props
def __init__(self):
"""
Constructor
"""
self.actionLayer = ActionLayer() # see actionLayer.py
self.propositionLayer = PropositionLayer() # see propositionLayer.py
def getPropositionLayer(self):
# returns the proposition layer
return self.propositionLayer
def setPropositionLayer(self, propLayer):
# sets the proposition layer
self.propositionLayer = propLayer
def getActionLayer(self):
# returns the action layer
return self.actionLayer
def setActionLayer(self, actionLayer):
# sets the action layer
self.actionLayer = actionLayer
def updateActionLayer(self, previousPropositionLayer):
"""
Updates the action layer given the previous proposition layer (see propositionLayer.py)
You should add an action to the layer if its preconditions are in the previous propositions layer,
and the preconditions are not pairwise mutex.
allAction is the list of all the action (include noOp) in the domain
You might want to use those functions:
previousPropositionLayer.isMutex(prop1, prop2) returns true if prop1 and prop2 are mutex at the previous propositions layer
previousPropositionLayer.allPrecondsInLayer(action) returns true if all the preconditions of action are in the previous propositions layer
self.actionLayer.addAction(action) adds action to the current action layer
"""
allActions = PlanGraphLevel.actions
for action in allActions:
if previousPropositionLayer.allPrecondsInLayer(action) \
and True not in [previousPropositionLayer.isMutex(p1, p2) for p1, p2 in exclusiveProduct(action.getPre(), action.getPre())]:
self.actionLayer.addAction(action)
def updateMutexActions(self, previousLayerMutexProposition):
"""
Updates the mutex list in self.actionLayer,
given the mutex proposition from the previous layer.
currentLayerActions are the actions in the current action layer
You might want to use this function:
self.actionLayer.addMutexActions(action1, action2)
adds the pair (action1, action2) to the mutex list in the current action layer
Note that action is *not* mutex with itself
"""
currentLayerActions = self.actionLayer.getActions()
actionPairs = exclusiveProduct(currentLayerActions, currentLayerActions)
for a1, a2 in actionPairs:
if mutexActions(a1, a2, previousLayerMutexProposition):
self.actionLayer.addMutexActions(a1, a2)
def updatePropositionLayer(self):
"""
Updates the propositions in the current proposition layer,
given the current action layer.
don't forget to update the producers list!
Note that same proposition in different layers might have different producers lists,
hence you should create two different instances.
currentLayerActions is the list of all the actions in the current layer.
You might want to use those functions:
dict() creates a new dictionary that might help to keep track on the propositions that you've
already added to the layer
self.propositionLayer.addProposition(prop) adds the proposition prop to the current layer
"""
currentLayerActions = self.actionLayer.getActions()
props = dict()
for a in currentLayerActions:
for prop in a.getAdd():
name = prop.getName()
if name not in props.keys():
props[name] = Proposition(name)
props[name].addProducer(a)
for prop in props.values():
self.propositionLayer.addProposition(prop)
def updateMutexProposition(self):
"""
updates the mutex propositions in the current proposition layer
You might want to use those functions:
mutexPropositions(prop1, prop2, currentLayerMutexActions) returns true if prop1 and prop2 are mutex in the current layer
self.propositionLayer.addMutexProp(prop1, prop2) adds the pair (prop1, prop2) to the mutex list of the current layer
"""
currentLayerPropositions = self.propositionLayer.getPropositions()
currentLayerMutexActions = self.actionLayer.getMutexActions()
for p1, p2 in exclusiveProduct(currentLayerPropositions, currentLayerPropositions):
if mutexPropositions(p1, p2, currentLayerMutexActions):
self.propositionLayer.addMutexProp(p1, p2)
def expand(self, previousLayer):
"""
Your algorithm should work as follows:
First, given the propositions and the list of mutex propositions from the previous layer,
set the actions in the action layer.
Then, set the mutex action in the action layer.
Finally, given all the actions in the current layer,
set the propositions and their mutex relations in the proposition layer.
"""
previousPropositionLayer = previousLayer.getPropositionLayer()
previousLayerMutexProposition = previousPropositionLayer.getMutexProps()
self.updateActionLayer(previousPropositionLayer)
self.updateMutexActions(previousLayerMutexProposition)
self.updatePropositionLayer()
self.updateMutexProposition()
def expandWithoutMutex(self, previousLayer):
"""
Expand the graph without updating the mutex relations
"""
previousPropositionLayer = previousLayer.getPropositionLayer()
self.updateActionLayer(previousPropositionLayer)
self.updatePropositionLayer()
class PlanGraphLevel(object):
"""
A class for representing a level in the plan graph.
For each level i, the PlanGraphLevel consists of the actionLayer and propositionLayer at this level in this order!
"""
independentActions = None # updated to the independentActions of the propblem GraphPlan.py line 25
actions = None # updated to the actions of the problem GraphPlan.py line 26 and planningProblem.py line 25
props = None # updated to the propositions of the problem GraphPlan.py line 27 and planningProblem.py line 26
@staticmethod
def setIndependentActions(independentActions):
PlanGraphLevel.independentActions = independentActions
@staticmethod
def setActions(actions):
PlanGraphLevel.actions = actions
@staticmethod
def setProps(props):
PlanGraphLevel.props = props
def __init__(self):
self.actionLayer = ActionLayer() # see actionLayer.py
self.propositionLayer = PropositionLayer() # see propositionLayer.py
def getPropositionLayer(self): # returns the proposition layer
return self.propositionLayer
def setPropositionLayer(self, propLayer): # sets the proposition layer
self.propositionLayer = propLayer
def getActionLayer(self): # returns the action layer
return self.actionLayer
def setActionLayer(self, actionLayer): # sets the action layer
self.actionLayer = actionLayer
def updateActionLayer(self, previousPropositionLayer):
"""
Updates the action layer given the previous proposition layer (see propositionLayer.py)
You should add an action to the layer if its preconditions are in the previous propositions layer,
and the preconditions are not pairwise mutex.
allAction is the set of all the action (include noOp) in the domain
You might want to use those functions:
previousPropositionLayer.isMutex(prop1, prop2) returns true if prop1 and prop2 are mutex at the previous propositions layer
previousPropositionLayer.allPrecondsInLayer(action) returns true if all the preconditions of action are in the previous propositions layer
self.actionLayer.addAction(action) adds action to the current action layer
"""
allActions = PlanGraphLevel.actions
"*** YOUR CODE HERE ***"
for a in allActions:
if previousPropositionLayer.allPrecondsInLayer(a):
self.actionLayer.addAction(a)
def updateMutexActions(self, previousLayerMutexProposition):
"""
Updates the mutex set in self.actionLayer,
given the mutex proposition from the previous layer.
currentLayerActions are the actions in the current action layer
You might want to use this function:
self.actionLayer.addMutexActions(action1, action2)
adds the pair (action1, action2) to the mutex set in the current action layer
Note that action is *not* mutex with itself
"""
currentLayerActions = self.actionLayer.getActions()
"*** YOUR CODE HERE ***"
for a_i in currentLayerActions:
for a_j in currentLayerActions:
if a_i != a_j and mutexActions(a_i, a_j, previousLayerMutexProposition):
if Pair(a_i,a_j) not in self.actionLayer.mutexActions:
self.actionLayer.addMutexActions(a_i,a_j)
def updatePropositionLayer(self):
"""
Updates the propositions in the current proposition layer,
given the current action layer.
don't forget to update the producers list!
Note that same proposition in different layers might have different producers lists,
hence you should create two different instances.
currentLayerActions is the set of all the actions in the current layer.
You might want to use those functions:
dict() creates a new dictionary that might help to keep track on the propositions that you've
already added to the layer
self.propositionLayer.addProposition(prop) adds the proposition prop to the current layer
"""
currentLayerActions = self.actionLayer.getActions()
"*** YOUR CODE HERE ***"
for a in currentLayerActions:
for p in a.getAdd():
if p not in self.propositionLayer.getPropositions():
self.propositionLayer.addProposition(p)
p.addProducer(a)
def updateMutexProposition(self):
"""
updates the mutex propositions in the current proposition layer
You might want to use those functions:
mutexPropositions(prop1, prop2, currentLayerMutexActions) returns true if prop1 and prop2 are mutex in the current layer
self.propositionLayer.addMutexProp(prop1, prop2) adds the pair (prop1, prop2) to the mutex set of the current layer
"""
currentLayerPropositions = self.propositionLayer.getPropositions()
currentLayerMutexActions = self.actionLayer.getMutexActions()
"*** YOUR CODE HERE ***"
for p_i in currentLayerPropositions:
for p_j in currentLayerPropositions:
if p_i != p_j and mutexPropositions(p_i,p_j,currentLayerMutexActions):
if Pair(p_i,p_j) not in self.propositionLayer.mutexPropositions:
self.propositionLayer.mutexPropositions.append(Pair(p_i,p_j))
def expand(self, previousLayer):
"""
Your algorithm should work as follows:
First, given the propositions and the list of mutex propositions from the previous layer,
set the actions in the action layer.
Then, set the mutex action in the action layer.
Finally, given all the actions in the current layer,
set the propositions and their mutex relations in the proposition layer.
"""
previousPropositionLayer = previousLayer.getPropositionLayer()
previousLayerMutexProposition = previousPropositionLayer.getMutexProps()
"*** YOUR CODE HERE ***"
self.updateActionLayer(previousPropositionLayer)
self.updateMutexActions(previousLayerMutexProposition)
self.updatePropositionLayer()
self.updateMutexProposition()
def expandWithoutMutex(self, previousLayer):
"""
Questions 11 and 12
You don't have to use this function
"""
previousLayerProposition = previousLayer.getPropositionLayer()
"*** YOUR CODE HERE ***"
self.updateActionLayer(previousLayerProposition)
self.updatePropositionLayer()
def mutexActions(a1, a2, mutexProps):
"""
This function returns true if a1 and a2 are mutex actions.
We first check whether a1 and a2 are in PlanGraphLevel.independentActions,
this is the list of all the independent pair of actions (according to your implementation in question 1).
If not, we check whether a1 and a2 have competing needs
"""
if Pair(a1,a2) not in PlanGraphLevel.independentActions:
return True
return haveCompetingNeeds(a1, a2, mutexProps)
def haveCompetingNeeds(a1, a2, mutexProps):
pre1 = a1.getPre()
pre2 = a2.getPre()
"""
Complete code for deciding whether actions a1 and a2 have competing needs,
given the mutex proposition from previous level (list of pairs of propositions).
Hint: for propositions p and q, the command "Pair(p, q) in mutexProps"
returns true if p and q are mutex in the previous level
"""
"*** YOUR CODE HERE ***"
# Get preconditions of both actions
# Competing needs: Check if a1 and a2 have preconditions that are mutex
for p1 in pre1:
for p2 in pre2:
if Pair(p1, p2) in mutexProps:
return True
return False
def mutexPropositions(prop1, prop2, mutexActions):
prod1 = prop1.getProducers()
prod2 = prop2.getProducers()
"""
complete code for deciding whether two propositions are mutex,
given the mutex action from the current level (set of pairs of actions).
Your updateMutexProposition function should call this function
You might want to use this function:
prop1.getProducers() returns the set of all the possible actions in the layer that have prop1 on their add list
"""
"*** YOUR CODE HERE ***"
for a1 in prod1:
for a2 in prod2:
# Check if all actions are pairwise mutex
if Pair(a1,a2) not in mutexActions:
return False
return True
class PlanGraphLevel(object):
"""
A class for representing a level in the plan graph.
For each level i, the PlanGraphLevel consists of the actionLayer and propositionLayer at this level in this order!
"""
independentActions = [] # updated to the independentActions of the propblem GraphPlan.py line 31
actions = [] # updated to the actions of the problem GraphPlan.py line 32 and planningProblem.py line 25
props = [] # updated to the propositions of the problem GraphPlan.py line 33 and planningProblem.py line 26
@staticmethod
def setIndependentActions(independentActions):
PlanGraphLevel.independentActions = independentActions
@staticmethod
def setActions(actions):
PlanGraphLevel.actions = actions
@staticmethod
def setProps(props):
PlanGraphLevel.props = props
def __init__(self):
"""
Constructor
"""
self.actionLayer = ActionLayer() # see actionLayer.py
self.propositionLayer = PropositionLayer() # see propositionLayer.py
def getPropositionLayer(self):
# returns the proposition layer
return self.propositionLayer
def setPropositionLayer(self, propLayer):
# sets the proposition layer
self.propositionLayer = propLayer
def getActionLayer(self):
# returns the action layer
return self.actionLayer
def setActionLayer(self, actionLayer):
# sets the action layer
self.actionLayer = actionLayer
def updateActionLayer(self, previousPropositionLayer):
"""
Updates the action layer given the previous proposition layer (see propositionLayer.py)
You should add an action to the layer if its preconditions are in the previous propositions layer,
and the preconditions are not pairwise mutex.
allAction is the list of all the action (include noOp) in the domain
You might want to use those functions:
previousPropositionLayer.isMutex(prop1, prop2) returns true if prop1 and prop2 are mutex at the previous propositions layer
previousPropositionLayer.allPrecondsInLayer(action) returns true if all the preconditions of action are in the previous propositions layer
self.actionLayer.addAction(action) adds action to the current action layer
"""
allActions = PlanGraphLevel.actions
for action in allActions:
if previousPropositionLayer.allPrecondsInLayer(action) and\
not pre_contains_pairwise_mutex(action, previousPropositionLayer):
self.actionLayer.addAction(action)
def updateMutexActions(self, previousLayerMutexProposition):
"""
Updates the mutex list in self.actionLayer,
given the mutex proposition from the previous layer.
currentLayerActions are the actions in the current action layer
You might want to use this function:
self.actionLayer.addMutexActions(action1, action2)
adds the pair (action1, action2) to the mutex list in the current action layer
Note that action is *not* mutex with itself
"""
currentLayerActions = self.actionLayer.getActions()
for action1 in currentLayerActions:
for action2 in currentLayerActions:
if action1 != action2 and\
mutexActions(action1, action2, previousLayerMutexProposition):
self.actionLayer.addMutexActions(action1, action2)
def updatePropositionLayer(self):
"""
Updates the propositions in the current proposition layer,
given the current action layer.
don't forget to update the producers list!
Note that same proposition in different layers might have different producers lists,
hence you should create two different instances.
currentLayerActions is the list of all the actions in the current layer.
You might want to use those functions:
dict() creates a new dictionary that might help to keep track on the propositions that you've
already added to the layer
self.propositionLayer.addProposition(prop) adds the proposition prop to the current layer
"""
currentLayerActions = self.actionLayer.getActions()
props_dict = dict()
for action in currentLayerActions:
for add_props in action.getAdd():
if add_props.getName() not in props_dict:
props_dict[add_props.getName()] = [action]
else:
props_dict[add_props.getName()] = \
props_dict[add_props.getName()]+[action]
for key in props_dict:
prop_temp = Proposition(key)
prop_temp.setProducers(props_dict[key]) #takes care of adding procedure
self.propositionLayer.addProposition(prop_temp)
#notice there is no mention of mutexes. is this ok?
def updateMutexProposition(self):
"""
updates the mutex propositions in the current proposition layer
You might want to use those functions:
mutexPropositions(prop1, prop2, currentLayerMutexActions) returns true if prop1 and prop2 are mutex in the current layer
self.propositionLayer.addMutexProp(prop1, prop2) adds the pair (prop1, prop2) to the mutex list of the current layer
"""
currentLayerPropositions = self.propositionLayer.getPropositions()
currentLayerMutexActions = self.actionLayer.getMutexActions()
for prop1 in currentLayerPropositions:
for prop2 in currentLayerPropositions:
if mutexPropositions(prop1, prop2, currentLayerMutexActions) \
and prop1 != prop2:
self.propositionLayer.addMutexProp(prop1, prop2)
def expand(self, previousLayer):
"""
Your algorithm should work as follows:
First, given the propositions and the list of mutex propositions from the previous layer,
set the actions in the action layer.
Then, set the mutex action in the action layer.
Finally, given all the actions in the current layer,
set the propositions and their mutex relations in the proposition layer.
"""
previousPropositionLayer = previousLayer.getPropositionLayer()
previousLayerMutexProposition = previousPropositionLayer.getMutexProps()
self.updateActionLayer(previousPropositionLayer)
self.updateMutexActions(previousLayerMutexProposition)
self.updatePropositionLayer()
self.updateMutexProposition()
def expandWithoutMutex(self, previousLayer):
"""
Questions 11 and 12
You don't have to use this function
"""
previousPropositionLayer = previousLayer.getPropositionLayer()
self.updateActionLayer(previousPropositionLayer)
self.updatePropositionLayer()
def __init__(self): """ Constructor """ self.actionLayer = ActionLayer() # see actionLayer.py self.propositionLayer = PropositionLayer() # see propositionLayer.py
class PlanGraphLevel(object):
"""
A class for representing a level in the plan graph.
For each level i, the PlanGraphLevel consists of the actionLayer and propositionLayer at this level in this order!
"""
independentActions = [
] # updated to the independentActions of the propblem GraphPlan.py line 31
actions = [
] # updated to the actions of the problem GraphPlan.py line 32 and planningProblem.py line 25
props = [
] # updated to the propositions of the problem GraphPlan.py line 33 and planningProblem.py line 26
@staticmethod
def setIndependentActions(independentActions):
PlanGraphLevel.independentActions = independentActions
@staticmethod
def setActions(actions):
PlanGraphLevel.actions = actions
@staticmethod
def setProps(props):
PlanGraphLevel.props = props
def __init__(self):
"""
Constructor
"""
self.actionLayer = ActionLayer() # see actionLayer.py
self.propositionLayer = PropositionLayer() # see propositionLayer.py
def getPropositionLayer(self):
# returns the proposition layer
return self.propositionLayer
def setPropositionLayer(self, propLayer):
# sets the proposition layer
self.propositionLayer = propLayer
def getActionLayer(self):
# returns the action layer
return self.actionLayer
def setActionLayer(self, actionLayer):
# sets the action layer
self.actionLayer = actionLayer
def updateActionLayer(self, previousPropositionLayer):
"""
Updates the action layer given the previous proposition layer (see propositionLayer.py)
You should add an action to the layer if its preconditions are in the previous propositions layer,
and the preconditions are not pairwise mutex.
allAction is the list of all the action (include noOp) in the domain
You might want to use those functions:
previousPropositionLayer.isMutex(prop1, prop2) returns true if prop1 and prop2 are mutex at the previous propositions layer
previousPropositionLayer.allPrecondsInLayer(action) returns true if all the preconditions of action are in the previous propositions layer
self.actionLayer.addAction(action) adds action to the current action layer
"""
allActions = PlanGraphLevel.actions
for action in allActions:
if previousPropositionLayer.allPrecondsInLayer(action) \
and True not in [previousPropositionLayer.isMutex(p1, p2) for p1, p2 in exclusiveProduct(action.getPre(), action.getPre())]:
self.actionLayer.addAction(action)
def updateMutexActions(self, previousLayerMutexProposition):
"""
Updates the mutex list in self.actionLayer,
given the mutex proposition from the previous layer.
currentLayerActions are the actions in the current action layer
You might want to use this function:
self.actionLayer.addMutexActions(action1, action2)
adds the pair (action1, action2) to the mutex list in the current action layer
Note that action is *not* mutex with itself
"""
currentLayerActions = self.actionLayer.getActions()
actionPairs = exclusiveProduct(currentLayerActions,
currentLayerActions)
for a1, a2 in actionPairs:
if mutexActions(a1, a2, previousLayerMutexProposition):
self.actionLayer.addMutexActions(a1, a2)
def updatePropositionLayer(self):
"""
Updates the propositions in the current proposition layer,
given the current action layer.
don't forget to update the producers list!
Note that same proposition in different layers might have different producers lists,
hence you should create two different instances.
currentLayerActions is the list of all the actions in the current layer.
You might want to use those functions:
dict() creates a new dictionary that might help to keep track on the propositions that you've
already added to the layer
self.propositionLayer.addProposition(prop) adds the proposition prop to the current layer
"""
currentLayerActions = self.actionLayer.getActions()
props = dict()
for a in currentLayerActions:
for prop in a.getAdd():
name = prop.getName()
if name not in props.keys():
props[name] = Proposition(name)
props[name].addProducer(a)
for prop in props.values():
self.propositionLayer.addProposition(prop)
def updateMutexProposition(self):
"""
updates the mutex propositions in the current proposition layer
You might want to use those functions:
mutexPropositions(prop1, prop2, currentLayerMutexActions) returns true if prop1 and prop2 are mutex in the current layer
self.propositionLayer.addMutexProp(prop1, prop2) adds the pair (prop1, prop2) to the mutex list of the current layer
"""
currentLayerPropositions = self.propositionLayer.getPropositions()
currentLayerMutexActions = self.actionLayer.getMutexActions()
for p1, p2 in exclusiveProduct(currentLayerPropositions,
currentLayerPropositions):
if mutexPropositions(p1, p2, currentLayerMutexActions):
self.propositionLayer.addMutexProp(p1, p2)
def expand(self, previousLayer):
"""
Your algorithm should work as follows:
First, given the propositions and the list of mutex propositions from the previous layer,
set the actions in the action layer.
Then, set the mutex action in the action layer.
Finally, given all the actions in the current layer,
set the propositions and their mutex relations in the proposition layer.
"""
previousPropositionLayer = previousLayer.getPropositionLayer()
previousLayerMutexProposition = previousPropositionLayer.getMutexProps(
)
self.updateActionLayer(previousPropositionLayer)
self.updateMutexActions(previousLayerMutexProposition)
self.updatePropositionLayer()
self.updateMutexProposition()
def expandWithoutMutex(self, previousLayer):
"""
Expand the graph without updating the mutex relations
"""
previousPropositionLayer = previousLayer.getPropositionLayer()
self.updateActionLayer(previousPropositionLayer)
self.updatePropositionLayer()
class PlanGraphLevel(object):
"""
A class for representing a level in the plan graph.
For each level i, the PlanGraphLevel consists of the actionLayer and propositionLayer at this level in this order!
"""
independentActions = [] # updated to the independentActions of the propblem GraphPlan.py line 31
actions = [] # updated to the actions of the problem GraphPlan.py line 32 and planningProblem.py line 25
props = [] # updated to the propositions of the problem GraphPlan.py line 33 and planningProblem.py line 26
@staticmethod
def setIndependentActions(independentActions):
PlanGraphLevel.independentActions = independentActions
@staticmethod
def setActions(actions):
PlanGraphLevel.actions = actions
@staticmethod
def setProps(props):
PlanGraphLevel.props = props
def __init__(self):
"""
Constructor
"""
self.actionLayer = ActionLayer() # see actionLayer.py
self.propositionLayer = PropositionLayer() # see propositionLayer.py
def getPropositionLayer(self):
return self.propositionLayer
def setPropositionLayer(self, propLayer):
self.propositionLayer = propLayer
def getActionLayer(self):
return self.actionLayer
def setActionLayer(self, actionLayer):
self.actionLayer = actionLayer
def updateActionLayer(self, previousPropositionLayer):
"""
Updates the action layer given the previous proposition layer (see propositionLayer.py)
allAction is the list of all the action (include noOp in the domain)
"""
allActions = PlanGraphLevel.actions
for action in allActions:
if previousPropositionLayer.allPrecondsInLayer(action):
self.actionLayer.addAction(action)
for p1 in action.getPre():
for p2 in action.getPre():
if previousPropositionLayer.isMutex(p1, p2):
self.actionLayer.removeActions(action)
def updateMutexActions(self, previousLayerMutexProposition):
"""
Updates the mutex list in self.actionLayer,
given the mutex proposition from the previous layer.
currentLayerActions are the actions in the current action layer
"""
currentLayerActions = self.actionLayer.getActions()
for a1 in currentLayerActions:
for a2 in currentLayerActions:
if a1 == a2:
continue
if mutexActions(a1, a2, previousLayerMutexProposition):
self.actionLayer.addMutexActions(a1, a2)
def updatePropositionLayer(self):
"""
Updates the propositions in the current proposition layer,
given the current action layer.
don't forget to update the producers list!
"""
currentLayerActions = self.actionLayer.getActions()
propsToAdd = dict()
for action in currentLayerActions:
for prop in action.getAdd():
if prop.getName() not in propsToAdd:
propsToAdd[prop.getName()] = Proposition(prop.getName())
temp = propsToAdd[prop.getName()]
if action not in temp.getProducers():
temp.addProducer(action)
for prop in propsToAdd.values():
self.propositionLayer.addProposition(prop)
def updateMutexProposition(self):
"""
updates the mutex propositions in the current proposition layer
"""
currentLayerPropositions = self.propositionLayer.getPropositions()
currentLayerMutexActions = self.actionLayer.getMutexActions()
for prop1 in currentLayerPropositions:
for prop2 in currentLayerPropositions:
if prop1 == prop2:
continue
if mutexPropositions(prop1, prop2, currentLayerMutexActions):
self.propositionLayer.addMutexProp(prop1, prop2)
def expand(self, previousLayer):
"""
Your algorithm should work as follows:
First, given the propositions and the list of mutex propositions from the previous layer,
set the actions in the action layer.
Then, set the mutex action in the action layer.
Finally, given all the actions in the current layer, set the propositions and their mutex relations in the proposition layer.
"""
previousPropositionLayer = previousLayer.getPropositionLayer()
previousLayerMutexProposition = previousPropositionLayer.getMutexProps()
self.updateActionLayer(previousPropositionLayer)
self.updateMutexActions(previousLayerMutexProposition)
self.updatePropositionLayer()
self.updateMutexProposition()
def expandWithoutMutex(self, previousLayer):
"""
Questions 11 and 12
You don't have to use this function
"""
previousLayerProposition = previousLayer.getPropositionLayer()
"*** YOUR CODE HERE ***"
class PlanGraphLevel(object):
"""
A class for representing a level in the plan graph.
For each level i, the PlanGraphLevel consists of the actionLayer and propositionLayer at this level in this order!
"""
independentActions = None # updated to the independentActions of the propblem GraphPlan.py line 25
actions = None # updated to the actions of the problem GraphPlan.py line 26 and planningProblem.py line 25
props = None # updated to the propositions of the problem GraphPlan.py line 27 and planningProblem.py line 26
@staticmethod
def setIndependentActions(independentActions):
PlanGraphLevel.independentActions = independentActions
@staticmethod
def setActions(actions):
PlanGraphLevel.actions = actions
@staticmethod
def setProps(props):
PlanGraphLevel.props = props
def __init__(self):
"""
Constructor
"""
self.actionLayer = ActionLayer() # see actionLayer.py
self.propositionLayer = PropositionLayer() # see propositionLayer.py
def getPropositionLayer(self): # returns the proposition layer
return self.propositionLayer
def setPropositionLayer(self, propLayer): # sets the proposition layer
self.propositionLayer = propLayer
def getActionLayer(self): # returns the action layer
return self.actionLayer
def setActionLayer(self, actionLayer): # sets the action layer
self.actionLayer = actionLayer
def updateActionLayer(self, previousPropositionLayer):
"""
Updates the action layer given the previous proposition layer (see propositionLayer.py)
You should add an action to the layer if its preconditions are in the previous propositions layer,
and the preconditions are not pairwise mutex.
allAction is the set of all the action (include noOp) in the domain
You might want to use those functions:
previousPropositionLayer.isMutex(prop1, prop2) returns true if prop1 and prop2 are mutex at the previous propositions layer
previousPropositionLayer.allPrecondsInLayer(action) returns true if all the preconditions of action are in the previous propositions layer
self.actionLayer.addAction(action) adds action to the current action layer
"""
allActions = PlanGraphLevel.actions
for action in allActions:
if previousPropositionLayer.allPrecondsInLayer(action):
pre = action.getPre()
add = True
for prop1 in pre:
for prop2 in pre:
if prop1 != prop2 and previousPropositionLayer.isMutex(prop1, prop2):
add = False
break
if not add:
break
if add:
self.actionLayer.addAction(action)
def updateMutexActions(self, previousLayerMutexProposition):
"""
Updates the mutex set in self.actionLayer,
given the mutex proposition from the previous layer.
currentLayerActions are the actions in the current action layer
You might want to use this function:
self.actionLayer.addMutexActions(action1, action2)
adds the pair (action1, action2) to the mutex set in the current action layer
Note that action is *not* mutex with itself
"""
currentLayerActions = self.actionLayer.getActions()
for a1 in currentLayerActions:
for a2 in currentLayerActions:
if(a1 == a2):
continue
elif haveCompetingNeeds(a1, a2, previousLayerMutexProposition):
self.actionLayer.addMutexActions(a1, a2)
def updatePropositionLayer(self):
"""
Updates the propositions in the current proposition layer,
given the current action layer.
don't forget to update the producers list!
Note that same proposition in different layers might have different producers lists,
hence you should create two different instances.
currentLayerActions is the set of all the actions in the current layer.
You might want to use those functions:
dict() creates a new dictionary that might help to keep track on the propositions that you've
already added to the layer
self.propositionLayer.addProposition(prop) adds the proposition prop to the current layer
"""
currentLayerActions = self.actionLayer.getActions()
propostions = {}
for action in currentLayerActions:
for prop in action.getAdd():
if not prop.getName() in propostions:
newProp = Proposition(prop.getName())
propostions[prop.getName()] = newProp
propostions[prop.getName()].addProducer(action)
for prop in propostions:
self.propositionLayer.addProposition(propostions[prop])
def updateMutexProposition(self):
"""
updates the mutex propositions in the current proposition layer
You might want to use those functions:
mutexPropositions(prop1, prop2, currentLayerMutexActions) returns true if prop1 and prop2 are mutex in the current layer
self.propositionLayer.addMutexProp(prop1, prop2) adds the pair (prop1, prop2) to the mutex set of the current layer
"""
currentLayerPropositions = self.propositionLayer.getPropositions()
currentLayerMutexActions = self.actionLayer.getMutexActions()
for prop1 in currentLayerPropositions:
for prop2 in currentLayerPropositions:
if prop1 == prop2:
continue
elif mutexPropositions(prop1, prop2, currentLayerMutexActions):
self.propositionLayer.addMutexProp(prop1, prop2)
def expand(self, previousLayer):
"""
Your algorithm should work as follows:
First, given the propositions and the list of mutex propositions from the previous layer,
set the actions in the action layer.
Then, set the mutex action in the action layer.
Finally, given all the actions in the current layer,
set the propositions and their mutex relations in the proposition layer.
"""
previousPropositionLayer = previousLayer.getPropositionLayer()
previousLayerMutexProposition = previousPropositionLayer.getMutexProps()
self.updateActionLayer(previousPropositionLayer)
self.updateMutexActions(previousLayerMutexProposition)
self.updatePropositionLayer()
self.updateMutexProposition()
def expandWithoutMutex(self, previousLayer):
"""
Questions 11 and 12
You don't have to use this function
"""
previousLayerProposition = previousLayer.getPropositionLayer()
self.updateActionLayer(previousLayerProposition)
self.updatePropositionLayer()
class PlanGraphLevel(object):
"""
A class for representing a level in the plan graph.
For each level i, the PlanGraphLevel consists of the actionLayer and propositionLayer at this level in this order!
"""
independentActions = [
] # updated to the independentActions of the propblem GraphPlan.py line 31
actions = [
] # updated to the actions of the problem GraphPlan.py line 32 and planningProblem.py line 25
props = [
] # updated to the propositions of the problem GraphPlan.py line 33 and planningProblem.py line 26
@staticmethod
def setIndependentActions(independentActions):
PlanGraphLevel.independentActions = independentActions
@staticmethod
def setActions(actions):
PlanGraphLevel.actions = actions
@staticmethod
def setProps(props):
PlanGraphLevel.props = props
def __init__(self):
"""
Constructor
"""
self.actionLayer = ActionLayer() # see actionLayer.py
self.propositionLayer = PropositionLayer() # see propositionLayer.py
def getPropositionLayer(self):
# returns the proposition layer
return self.propositionLayer
def setPropositionLayer(self, propLayer):
# sets the proposition layer
self.propositionLayer = propLayer
def getActionLayer(self):
# returns the action layer
return self.actionLayer
def setActionLayer(self, actionLayer):
# sets the action layer
self.actionLayer = actionLayer
def updateActionLayer(self, previousPropositionLayer):
"""
Updates the action layer given the previous proposition layer (see propositionLayer.py)
You should add an action to the layer if its preconditions are in the previous propositions layer,
and the preconditions are not pairwise mutex.
allAction is the list of all the action (include noOp) in the domain
You might want to use those functions:
previousPropositionLayer.isMutex(prop1, prop2) returns true if prop1 and prop2 are mutex at the previous propositions layer
previousPropositionLayer.allPrecondsInLayer(action) returns true if all the preconditions of action are in the previous propositions layer
self.actionLayer.addAction(action) adds action to the current action layer
"""
allActions = PlanGraphLevel.actions
for action in allActions:
if previousPropositionLayer.allPrecondsInLayer(
action) and action not in self.actionLayer.getActions():
self.actionLayer.addAction(action)
def updateMutexActions(self, previousLayerMutexProposition):
"""
Updates the mutex list in self.actionLayer,
given the mutex proposition from the previous layer.
currentLayerActions are the actions in the current action layer
You might want to use this function:
self.actionLayer.addMutexActions(action1, action2)
adds the pair (action1, action2) to the mutex list in the current action layer
Note that action is *not* mutex with itself
"""
currentLayerActions = self.actionLayer.getActions()
for a1 in currentLayerActions:
for a2 in currentLayerActions:
if a1 == a2:
continue # an action is not mutex with itself
if Pair(a1, a2) not in self.actionLayer.getMutexActions(
) and mutexActions(a1, a2, previousLayerMutexProposition):
# if a1 and a2 are mutex actions, and we didn't add them before, we should do so now
self.actionLayer.addMutexActions(a1, a2)
def updatePropositionLayer(self):
"""
Updates the propositions in the current proposition layer,
given the current action layer.
don't forget to update the producers list!
Note that same proposition in different layers might have different producers lists,
hence you should create two different instances.
currentLayerActions is the list of all the actions in the current layer.
You might want to use those functions:
dict() creates a new dictionary that might help to keep track on the propositions that you've
already added to the layer
self.propositionLayer.addProposition(prop) adds the proposition prop to the current layer
"""
currentLayerActions = self.actionLayer.getActions()
for action in currentLayerActions:
for p in action.getAdd():
# add the proposition if it doesn't already exist
if p not in self.propositionLayer.getPropositions():
self.propositionLayer.addProposition(p)
# add the action as a producer if it doesn't already exist
if action not in p.getProducers():
p.addProducer(action)
def updateMutexProposition(self):
"""
updates the mutex propositions in the current proposition layer
You might want to use those functions:
mutexPropositions(prop1, prop2, currentLayerMutexActions) returns true if prop1 and prop2 are mutex in the current layer
self.propositionLayer.addMutexProp(prop1, prop2) adds the pair (prop1, prop2) to the mutex list of the current layer
"""
currentLayerPropositions = self.propositionLayer.getPropositions()
currentLayerMutexActions = self.actionLayer.getMutexActions()
for p in currentLayerPropositions:
for q in currentLayerPropositions:
if p == q:
continue # a proposition is not mutex with itself
if Pair(p, q) not in self.propositionLayer.getMutexProps(
) and mutexPropositions(p, q, currentLayerMutexActions):
# if p and q are mutex props, and we didn't add them before, we should do so now
self.propositionLayer.addMutexProp(p, q)
def expand(self, previousLayer):
"""
Your algorithm should work as follows:
First, given the propositions and the list of mutex propositions from the previous layer,
set the actions in the action layer.
Then, set the mutex action in the action layer.
Finally, given all the actions in the current layer,
set the propositions and their mutex relations in the proposition layer.
"""
previousPropositionLayer = previousLayer.getPropositionLayer()
previousLayerMutexProposition = previousPropositionLayer.getMutexProps(
)
self.updateActionLayer(previousPropositionLayer)
self.updateMutexActions(previousLayerMutexProposition)
self.updatePropositionLayer()
self.updateMutexProposition()
def expandWithoutMutex(self, previousLayer):
"""
Questions 11 and 12
You don't have to use this function
"""
previousLayerProposition = previousLayer.getPropositionLayer()
self.updateActionLayer(previousLayerProposition)
self.updatePropositionLayer()
class PlanGraphLevel(object):
"""
A class for representing a level in the plan graph.
For each level i, the PlanGraphLevel consists of the actionLayer and propositionLayer at this level in this order!
"""
independentActions = [] # updated to the independentActions of the propblem GraphPlan.py line 31
actions = [] # updated to the actions of the problem GraphPlan.py line 32 and planningProblem.py line 25
props = [] # updated to the propositions of the problem GraphPlan.py line 33 and planningProblem.py line 26
@staticmethod
def setIndependentActions(independentActions):
PlanGraphLevel.independentActions = independentActions
@staticmethod
def setActions(actions):
PlanGraphLevel.actions = actions
@staticmethod
def setProps(props):
PlanGraphLevel.props = props
def __init__(self):
"""
Constructor
"""
self.actionLayer = ActionLayer() # see actionLayer.py
self.propositionLayer = PropositionLayer() # see propositionLayer.py
def getPropositionLayer(self):
# returns the proposition layer
return self.propositionLayer
def setPropositionLayer(self, propLayer):
# sets the proposition layer
self.propositionLayer = propLayer
def getActionLayer(self):
# returns the action layer
return self.actionLayer
def setActionLayer(self, actionLayer):
# sets the action layer
self.actionLayer = actionLayer
def updateActionLayer(self, previousPropositionLayer):
"""
Updates the action layer given the previous proposition layer (see propositionLayer.py)
You should add an action to the layer if its preconditions are in the previous propositions layer,
and the preconditions are not pairwise mutex.
allAction is the list of all the action (include noOp) in the domain
You might want to use those functions:
previousPropositionLayer.isMutex(prop1, prop2) returns true if prop1 and prop2 are mutex at the previous propositions layer
previousPropositionLayer.allPrecondsInLayer(action) returns true if all the preconditions of action are in the previous propositions layer
self.actionLayer.addAction(action) adds action to the current action layer
"""
allActions = PlanGraphLevel.actions
"*** YOUR CODE HERE ***"
for action in allActions:
#check the preconditions
if previousPropositionLayer.allPrecondsInLayer(action):
actionPropositions = action.getPre()
#check the mutex
isNotMutex = True
for prop1 in actionPropositions:
for prop2 in actionPropositions:
if not prop1 == prop2:
if previousPropositionLayer.isMutex(prop1, prop2):
isNotMutex = False
if isNotMutex:
self.actionLayer.addAction(action)
def updateMutexActions(self, previousLayerMutexProposition):
"""
Updates the mutex list in self.actionLayer,
given the mutex proposition from the previous layer.
currentLayerActions are the actions in the current action layer
You might want to use this function:
self.actionLayer.addMutexActions(action1, action2)
adds the pair (action1, action2) to the mutex list in the current action layer
Note that action is *not* mutex with itself
"""
currentLayerActions = self.actionLayer.getActions()
"*** YOUR CODE HERE ***"
for action1 in currentLayerActions:
for action2 in currentLayerActions:
if (not action1 == action2) and\
(Pair(action1,action2) not in self.actionLayer.getMutexActions()):
if mutexActions(action1, action2, previousLayerMutexProposition):
self.actionLayer.addMutexActions(action1, action2)
def updatePropositionLayer(self):
"""
Updates the propositions in the current proposition layer,
given the current action layer.
don't forget to update the producers list!
Note that same proposition in different layers might have different producers lists,
hence you should create two different instances.
currentLayerActions is the list of all the actions in the current layer.
You might want to use those functions:
dict() creates a new dictionary that might help to keep track on the propositions that you've
already added to the layer
self.propositionLayer.addProposition(prop) adds the proposition prop to the current layer
"""
currentLayerActions = self.actionLayer.getActions()
"*** YOUR CODE HERE ***"
# create dictionary with proposition and actions
# leading to it
propositionDict = dict()
for action in currentLayerActions:
for prop in action.getAdd():
if prop.getName() in propositionDict.keys():
propositionDict[prop.getName()].append(action)
else:
actionList = list()
actionList.append(action)
propositionDict.update({prop.getName(): actionList})
#create proposition object and update their actions
for key in propositionDict.keys():
p = Proposition(key)
p.setProducers(propositionDict[key])
self.propositionLayer.addProposition(p)
def updateMutexProposition(self):
"""
updates the mutex propositions in the current proposition layer
You might want to use those functions:
mutexPropositions(prop1, prop2, currentLayerMutexActions) returns true if prop1 and prop2 are mutex in the current layer
self.propositionLayer.addMutexProp(prop1, prop2) adds the pair (prop1, prop2) to the mutex list of the current layer
"""
currentLayerPropositions = self.propositionLayer.getPropositions()
currentLayerMutexActions = self.actionLayer.getMutexActions()
"*** YOUR CODE HERE ***"
for prop1 in currentLayerPropositions:
for prop2 in currentLayerPropositions:
if not prop1 == prop2 and \
(Pair(prop1, prop2) not in self.propositionLayer.getMutexProps()):
if mutexPropositions(prop1, prop2, currentLayerMutexActions):
self.propositionLayer.addMutexProp(prop1, prop2)
def expand(self, previousLayer):
"""
Your algorithm should work as follows:
First, given the propositions and the list of mutex propositions from the previous layer,
set the actions in the action layer.
Then, set the mutex action in the action layer.
Finally, given all the actions in the current layer,
set the propositions and their mutex relations in the proposition layer.
"""
previousPropositionLayer = previousLayer.getPropositionLayer()
previousLayerMutexProposition = previousPropositionLayer.getMutexProps()
"*** YOUR CODE HERE ***"
self.updateActionLayer(previousPropositionLayer)
self.updateMutexActions(previousLayerMutexProposition)
self.updatePropositionLayer()
self.updateMutexProposition()
def expandWithoutMutex(self, previousLayer):
"""
Questions 11 and 12
You don't have to use this function
"""
previousLayerProposition = previousLayer.getPropositionLayer()
"*** YOUR CODE HERE ***"
self.updateActionLayer(previousLayerProposition)
self.updatePropositionLayer()
class PlanGraphLevel(object):
"""
A class for representing a level in the plan graph.
For each level i, the PlanGraphLevel consists of the actionLayer and propositionLayer at this level in this order!
"""
independentActions = [
] # updated to the independentActions of the propblem GraphPlan.py line 31
actions = [
] # updated to the actions of the problem GraphPlan.py line 32 and planningProblem.py line 25
props = [
] # updated to the propositions of the problem GraphPlan.py line 33 and planningProblem.py line 26
@staticmethod
def setIndependentActions(independentActions):
PlanGraphLevel.independentActions = independentActions
@staticmethod
def setActions(actions):
PlanGraphLevel.actions = actions
@staticmethod
def setProps(props):
PlanGraphLevel.props = props
def __init__(self):
"""
Constructor
"""
self.actionLayer = ActionLayer() # see actionLayer.py
self.propositionLayer = PropositionLayer() # see propositionLayer.py
def getPropositionLayer(self):
return self.propositionLayer
def setPropositionLayer(self, propLayer):
self.propositionLayer = propLayer
def getActionLayer(self):
return self.actionLayer
def setActionLayer(self, actionLayer):
self.actionLayer = actionLayer
def updateActionLayer(self, previousPropositionLayer):
"""
Updates the action layer given the previous proposition layer (see propositionLayer.py)
allAction is the list of all the actions (include noOp in the domain)
"""
allActions = PlanGraphLevel.actions
for a in allActions:
if previousPropositionLayer.allPrecondsInLayer(a):
self.actionLayer.addAction(a)
def updateMutexActions(self, previousLayerMutexProposition):
"""
Updates the mutex list in self.actionLayer,
given the mutex proposition from the previous layer.
currentLayerActions are the actions in the current action layer
"""
currentLayerActions = self.actionLayer.getActions()
for a_i in currentLayerActions:
for a_j in currentLayerActions:
if a_i != a_j and mutexActions(a_i, a_j,
previousLayerMutexProposition):
if Pair(a_i, a_j) not in self.actionLayer.mutexActions:
self.actionLayer.addMutexActions(a_i, a_j)
def updatePropositionLayer(self):
"""
Updates the propositions in the current proposition layer,
given the current action layer.
don't forget to update the producers list!
"""
currentLayerActions = self.actionLayer.getActions()
for a in currentLayerActions:
for p in a.getAdd():
if p not in self.propositionLayer.getPropositions():
self.propositionLayer.addProposition(p)
p.addProducer(a)
def updateMutexProposition(self):
"""
updates the mutex propositions in the current proposition layer
"""
currentLayerPropositions = self.propositionLayer.getPropositions()
currentLayerMutexActions = self.actionLayer.getMutexActions()
for p_i in currentLayerPropositions:
for p_j in currentLayerPropositions:
if p_i != p_j and mutexPropositions(p_i, p_j,
currentLayerMutexActions):
if Pair(p_i, p_j
) not in self.propositionLayer.mutexPropositions:
self.propositionLayer.mutexPropositions.append(
Pair(p_i, p_j))
def expand(self, previousLayer):
"""
Your algorithm should work as follows:
First, given the propositions and the list of mutex propositions from the previous layer,
set the actions in the action layer.
Then, set the mutex action in the action layer.
Finally, given all the actions in the current layer, set the propositions and their mutex relations in the proposition layer.
"""
previousPropositionLayer = previousLayer.getPropositionLayer()
previousLayerMutexProposition = previousPropositionLayer.getMutexProps(
)
self.updateActionLayer(previousPropositionLayer)
self.updateMutexActions(previousLayerMutexProposition)
self.updatePropositionLayer()
self.updateMutexProposition()
def expandWithoutMutex(self, previousLayer):
"""
Questions 11 and 12
You don't have to use this function
"""
previousLayerProposition = previousLayer.getPropositionLayer()
self.updateActionLayer(previousLayerProposition)
self.updatePropositionLayer()
