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Problem
(define (problem blackbox planner)
(:domain blackbox planner)
(:objects
G H X Grid table(5x5))
(:init
Location: ((5,1)(5,2)(4,1)(4,2)(4,3)(3,4)(1,2)(1,5)) Inaccesable
(G in Grid table, Location(4,4))
(H in Grid table Location(2,1))
(X in Grid table Location(3,3))
(clear G)
(clear H)
(clear X)
)
(:goal (and (at X G ) (at G H)))
)
Node
import heapq
class Node(object):
"""
Represent state of board in 8 puzzle problem.
"""
n = 0
def __init__(self, board, prev_state = None):
assert len(board) == 9
self.board = board[:];
self.prev = prev_state
self.step = 0
Node.n += 1
if self.prev:
self.step = self.prev.step + 1
def __eq__(self, other):
"""Check wether two state is equal."""
return self.board == other.board
def __hash__(self):
"""Return hash code of object.
Used for comparing elements in set
"""
h = [0, 0, 0]
h[0] = self.board[0] << 6 | self.board[1] << 3 | self.board[2]
h[1] = self.board[3] << 6 | self.board[4] << 3 | self.board[5]
h[2] = self.board[6] << 6 | self.board[7] << 3 | self.board[8]
h_val = 0
for h_i in h:
h_val = h_val * 31 + h_i
return h_val
def __str__(self):
string_list = [str(i) for i in self.board]
sub_list = (string_list[:3], string_list[3:6], string_list[6:])
return "\n".join([" ".join(l) for l in sub_list])
def manhattan_distance(self):
"""Return Manhattan distance of state."""
#TODO: return Manhattan distance
distance = 0
goal = [1,2,3,4,5,6,7,8,0]
for i in range(1,9):
xs, ys = self.__i2pos(self.board.index(i))
xg, yg = self.__i2pos(goal.index(i))
distance += abs(xs-xg) + abs(ys-yg)
return distance
def manhattan_score(self):
"""Return Manhattan score of state."""
#TODO: return Manhattan score of state
return 0
def hamming_distance(self):
"""Return Hamming distance of state."""
#TODO: return Hamming distance
distance = 0
goal = [1,2,3,4,5,6,7,8,0]
for i in range(9):
if goal[i] != self.board[i]: distance += 1
return distance
def hamming_score(self):
"""Return Hamming distance score of state."""
#TODO return Hamming score of state
return 0
def next(self):
"""Return next states from this state."""
next_moves = []
i = self.board.index(0)
next_moves = (self.move_up(i), self.move_down(i), self.move_left(i), self.move_right(i))
return [s for s in next_moves if s]
def move_right(self, i):
x, y = self.__i2pos(i)
if y < 2:
right_state = Node(self.board, self)
right = self.__pos2i(x, y+1)
right_state.__swap(i, right)
return right_state
def move_left(self, i):
x, y = self.__i2pos(i)
if y > 0:
left_state = Node(self.board, self)
left = self.__pos2i(x, y - 1)
left_state.__swap(i, left)
return left_state
def move_up(self, i):
x, y = self.__i2pos(i)
if x > 0:
up_state = Node(self.board, self)
up = self.__pos2i(x - 1, y)
up_state.__swap(i, up)
return up_state
def move_down(self, i):
x, y = self.__i2pos(i)
if x < 2:
down_state = Node(self.board, self)
down = self.__pos2i(x + 1, y)
down_state.__swap(i, down)
return down_state
def __swap(self, i, j):
self.board[j], self.board[i] = self.board[i], self.board[j]
def __i2pos(self, index):
return (int(index / 3), index % 3)
def __pos2i(self, x, y):
return x * 3 + y
class PriorityQueue:
def __init__(self):
self.heap = []
self.count = 0
def push(self, item, priority):
# FIXME: restored old behaviour to check against old results better
# FIXED: restored to stable behaviour
entry = (priority, self.count, item)
# entry = (priority, item)
heapq.heappush(self.heap, entry)
self.count += 1
def pop(self):
(_, _, item) = heapq.heappop(self.heap)
# (_, item) = heapq.heappop(self.heap)
return item
def isEmpty(self):
return len(self.heap) == 0
Searcher
from sys import argv
from time import time
from node import *
class Searcher(object):
def __init__(self, start, goal):
self.start = start
self.goal = goal
def print_path(self, state):
path = []
while state:
path.append(state)
state = state.prev
path.reverse()
print("\n-->\n".join([str(state) for state in path]))
def steepest_ascent_hill_climbing(self):
"""Run steepest ascent hill climbing search."""
#TODO Implement hill climbing.
stack = [self.start]
while stack:
state = stack.pop()
if state == self.goal:
self.print_path(state)
print "Find solution"
break
h_val = state.manhattan_distance() + state.hamming_distance()
next_state = False
for s in state.next():
h_val_next = s.manhattan_distance() + s.hamming_distance()
if h_val_next < h_val:
next_state = s
h_val = h_val_next
if next_state:
stack.append(next_state)
else:
self.print_path(state)
print "Cannot find solution"
# I don't know this function
def hill_climbing(self):
"""Run hill climbing search."""
#TODO Implement hill climbing.
stack = [self.start]
while stack:
state = stack.pop()
if state == self.goal:
self.print_path(state)
print "Find solution"
break
h_val = state.manhattan_distance() + state.hamming_distance()
next_state = False
for s in state.next():
h_val_next = s.manhattan_distance() + s.hamming_distance()
if h_val_next < h_val:
next_state = s
h_val = h_val_next
stack.append(next_state)
break
if not next_state:
self.print_path(state)
print "Cannot find solution"
if __name__ == "__main__":
script, strategy = argv
print("Search for solution\n")
start = Node([2,0,1,4,5,3,8,7,6])
goal = Node([1,2,3,4,5,6,7,8,0])
#print start.hamming_distance()
#print start.manhattan_distance()
search = Searcher(start, goal)
start_time = time()
if strategy == "hc":
search.hill_climbing()
elif strategy == "sahc":
search.steepest_ascent_hill_climbing()
else:
print "Wrong strategy"
end_time = time()
elapsed = end_time - start_time
print "Search time: %s" % elapsed
print "Number of initialized node: %d" % Node.n
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