def AStar(initial_state): global COUNT, BACKLINKS # priority queue with respective priority # add any auxiliary data structures as needed OPEN = PriorityQ() # inserts the initial state with a priority of 0 (priority for initial state is irrelevant to algorithm) OPEN.insert(initial_state, 0) CLOSED = [] BACKLINKS[initial_state] = None while OPEN.__len__() > 0: # S = min-priority state (priority value gets discarded, only state is looked at) S = OPEN.deletemin()[0] 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)==0: if True: print("COUNT = " + str(COUNT)) print("len(OPEN)=" + str(len(OPEN))) print("len(CLOSED)=" + str(len(CLOSED))) L = [] # looks at all possible new states from operations on S # if the new state has not already been put on CLOSED, append to L for op in Problem.OPERATORS: if op.precond(S): new_state = op.state_transf(S) #print(new_state.__str__()) if not occurs_in(new_state, CLOSED): L.append(new_state) BACKLINKS[new_state] = S # adds new states in L into OPEN with their priorities # if any state already occurs in OPEN... # if L's state has a lower priority, change OPEN's state priority to L's # otherwise, keep OPEN's state in OPEN, ignore L's for state in L: g_state = G(state) if OPEN.__contains__(state): if g_state < OPEN.getpriority(state): OPEN.remove(state) else: break OPEN.insert(state, g_state)
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