def AStar(initial_state):
global COUNT, BACKLINKS
# TODO: initialze and put first state into
# priority queue with respective priority
# add any auxiliary data structures as needed
OPEN = PriorityQ()
CLOSED = []
#define the g score which means
#the cost of the move will increase by 1 at each depth
g = {}
g[initial_state] = 0
BACKLINKS[initial_state] = None
#in priority queue, the state is the element
#and the F score is the priority
#F socre is the heuristics score plus g scorce
OPEN.insert(initial_state, g[initial_state] + 0)
while not OPEN.isEmpty():
COUNT += 1
S = OPEN.deletemin()
while S in CLOSED:
S = OPEN.deletemin()
CLOSED.append(S)
# DO NOT CHANGE THIS SECTION: begining
if Problem.GOAL_TEST(S):
print(Problem.GOAL_MESSAGE_FUNCTION(S))
path = backtrace(S)
return path, Problem.PROBLEM_NAME
# DO NOT CHANGE THIS SECTION: end
# TODO: finish A* implementation
for op in Problem.OPERATORS:
if op.precond(S):
new_state = op.state_transf(S)
if not (new_state in CLOSED):
h = heuristics(new_state)
g[new_state] = g[S] + 1
if new_state not in OPEN:
OPEN.insert(new_state, h + g[new_state])
BACKLINKS[new_state] = S
def AStar(initial_state):
# this breaks pq ties
global COUNT, BACKLINKS
# TODO: initialze and put first state into
# priority queue with respective priority
# add any auxiliary data structures as needed
OPEN = PriorityQ()
CLOSED = []
OPEN.insert(initial_state,0)
BACKLINKS[initial_state] = None
while not OPEN.isEmpty():
COUNT += 1
S = OPEN.deletemin()
while S in CLOSED:
S = OPEN.deletemin()
CLOSED.append(S)
# DO NOT CHANGE THIS SECTION: begining
if Problem.GOAL_TEST(S):
print(Problem.GOAL_MESSAGE_FUNCTION(S))
path = backtrace(S)
return path, Problem.PROBLEM_NAME
# DO NOT CHANGE THIS SECTION: end
# TODO: finish A* implementation
for op in Problem.OPERATORS:
#Optionally uncomment the following when debugging
#a new problem formulation.
# print("Trying operator: "+op.name)
if op.precond(S):
new_state = op.state_transf(S)
if not (new_state in CLOSED):
h = heuristics(new_state)
#new_pq_item = ( new_state,h)
OPEN.insert(new_state , h)
BACKLINKS[new_state] = S
def AStar(initial_state):
global COUNT, BACKLINKS
OPEN = PriorityQ() #currently discovered, not yet evaluated states
CLOSED = [] #already evaluated states
BACKLINKS[initial_state] = None #Tracks most efficient previous step
#Calculate F, G, H scores for Starting state
initialize_scores(initial_state)
#Only one state is known as of now
OPEN.insert(initial_state, F_SCORE[initial_state])
while OPEN.isEmpty() != True:
S, priority = OPEN.deletemin()
while S in CLOSED:
S = OPEN.deletemin()
CLOSED.append(S)
# DO NOT CHANGE THIS SECTION: beginning
if Problem.GOAL_TEST(S):
print(Problem.GOAL_MESSAGE_FUNCTION(S))
path = backtrace(S)
return path, Problem.PROBLEM_NAME
# DO NOT CHANGE THIS SECTION: end
COUNT += 1
if (COUNT % 32) == 0:
# if True:
# print(".",end="")
# if (COUNT % 128*128)==0:
if True:
print("COUNT = " + str(COUNT))
#print("len(OPEN)=" + str(len(OPEN))) #PriorityQ OPEN doesn't have len()
print("len(CLOSED)=" + str(len(CLOSED)))
for op in Problem.OPERATORS:
if op.precond(S):
new_state = op.state_transf(S)
if not occurs_in(new_state,
CLOSED): #ignore already evaluated neighbors
#find tentative score of neighbor
tentative_g_score = G_SCORE[S] + 1
if new_state not in G_SCORE: #Default INFINITY
BACKLINKS[
new_state] = S #First known path to new_state
elif tentative_g_score <= G_SCORE[new_state]:
BACKLINKS[
new_state] = S # Found better path to new_State
else:
continue #current path is not the best path to the neighbor
G_SCORE[new_state] = tentative_g_score
F_SCORE[new_state] = G_SCORE[new_state] + h_score_fn(
new_state)
# Delete previous F-score in PriorityQ if it exists
if OPEN.__contains__(new_state):
OPEN.remove(new_state)
#Update PriorityQ with new priority
OPEN.insert(new_state, F_SCORE[new_state])
# print(Problem.DESCRIBE_STATE(new_state))
#print(OPEN)
#Failure, if goal_test has not succeeded until now
print("COULD NOT FIND GOAL")
return
def AStar(initial_state):
# print("In RecDFS, with depth_limit="+str(depth_limit)+", current_state is ")
# print(Problem.DESCRIBE_STATE(current_state))
global COUNT, BACKLINKS
#Tracks most efficient previous step
BACKLINKS[initial_state] = None
#already evaluated states
CLOSED = []
#currently discovered, not yet evaluated states
OPEN = PriorityQ()
#Calculate F, G, H scores
initialize_scores(initial_state)
#Only initial node is known as of now
OPEN.insert(initial_state, F_SCORE[initial_state])
while OPEN.isEmpty() != True:
S = OPEN.deletemin()
CLOSED.append(S)
if Problem.GOAL_TEST(S):
print(Problem.GOAL_MESSAGE_FUNCTION(S))
backtrace(S)
return #FOUND GOAL
COUNT += 1
if (COUNT % 32) == 0:
# if True:
# print(".",end="")
# if (COUNT % 128*128)==0:
if True:
print("COUNT = " + str(COUNT))
#print("len(OPEN)=" + str(len(OPEN))) #PriorityQ OPEN doesn't have len()
print("len(CLOSED)=" + str(len(CLOSED)))
for op in Problem.OPERATORS:
if op.precond(S):
new_state = op.state_transf(S)
if not occurs_in(new_state,
CLOSED): #ignore already evaluated neighbors
#find tentative score of neighbor
tentative_g_score = G_SCORE[S] + 1
if new_state not in G_SCORE: #Default INFINITY
BACKLINKS[
new_state] = S #First known path to new_state
elif tentative_g_score >= G_SCORE[new_state]:
continue #current path is not the best path to the neighbor
else:
BACKLINKS[
new_state] = S #Found better path to new_State
G_SCORE[new_state] = tentative_g_score
F_SCORE[new_state] = G_SCORE[new_state] + h_score_fn(
new_state)
# discovered a new State
if not OPEN.__contains__(new_state):
OPEN.insert(new_state, F_SCORE[new_state])
# print(Problem.DESCRIBE_STATE(new_state))
#print(OPEN)
#Failure, if goal_test has not succeeded until now
print("COULD NOT FIND GOAL")
return
