def generalSearch(problem):
frontier=list() # create list for states in frontier
explored=list() # create list for explored states
paths=list() # crate list for possible paths
# statistics variable and list
nodegen = 0
branching = list()
frontier.append(problem) # append original problem to frontier
while 1:
if len(frontier) == 0: # if frontier is empty, no solution found
return False
# decide which state from the fronter to explore (always the 1st)
current_state=frontier.pop(0)
# add state chosen to explored set
explored.append(current_state)
# determine actions available fo current state
current_actions=rushDomain.actions(current_state)
localnode = 0
for x in current_actions: #for each action
next_state=rushDomain.results(current_state,x) # determine its result
if rushDomain.goaltest(next_state) is True: # if result is goal
paths.append(rushDomain.buildPath(next_state)) # calculate path to result and add it to paths
paths.append(nodegen)
paths.append(sum(branching) / float(len(branching)))
return paths # return first solution found
chkTemp=0
for exp in explored:
if rushDomain.cmpStates(next_state,exp):
chkTemp=-1
break
if chkTemp is not -1:
for fro in frontier:
if rushDomain.cmpStates(next_state,fro):
chkTemp=-1
break
if chkTemp is 0:
nodegen=nodegen+1
localnode=localnode+1
#frontier.insert(0,next_state) #depth search
frontier.append(next_state) #breath first search
branching.append(localnode)
def generalSearch(problem):
frontier=list() # create list for states in frontier
explored=list() # create list for explored states
paths=list() # crate list for possible paths
rushDomain.setHeuristic(problem,rushDomain.h(problem)) # set initial state heuristic value
frontier.append(problem) # append original problem to frontier
rushDomain.printState(problem)
while 1:
if len(frontier) == 0: # if frontier is empty, no solution found
return False
# get state with minimum heuristic
current_state=rushDomain.hmin(frontier)
# remove state chosen from frontier
frontier.remove(current_state)
# add state chosen to explored set
explored.append(current_state)
# determine actions available for current state
current_actions=rushDomain.actions(current_state)
print("---------------------------------------------------------")
rushDomain.printState(current_state)
print("actions:")
print(len(current_actions))
#raw_input("continue")
for x in current_actions: #for each action
next_state = rushDomain.results(current_state,x) # determine its result
if rushDomain.goaltest(next_state) is True: # if result is goal
# reconstruct solution path and add it to paths
paths.append(rushDomain.buildPath(next_state))
return paths # return first solution found
chkTemp = 0
for exp in explored:
# checks if new state belongs to explores and
# if it does checks if its depth is bigger than the explored state
# we don't want to add states with bigger depth to frontier
if rushDomain.cmpStates(next_state,exp) and rushDomain.stateDepth(next_state) >= rushDomain.stateDepth(exp):
chkTemp = -1 # new states with bigger depth are not added
break
if chkTemp is 0:
for fro in frontier:
# checks if new state belongs to the frontier
if rushDomain.cmpStates(next_state,fro):
# if new state has less depth than state in frontier, then
# delete state in frontier because new state will be added after
# and is better than the previous
if rushDomain.stateDepth(next_state) < rushDomain.stateDepth(fro):
frontier.remove(fro)
else:
chkTemp = -1
break;
# if new state doesn't belong to frontier or explored or has
# a depth lower that any other state, add state to frontier
if chkTemp is 0:
rushDomain.setHeuristic(next_state,rushDomain.h(next_state))
frontier.append(next_state)