def uniform_cost_search(board, print_steps=False):
"""
@param board: a Board object
@param print_steps: flag to print intermediate steps
@return (records, board)
records: a dictionary keeping track of necessary statistics
board: a copy of the board at the finished state.
Contains an array of all moves performed.
Performs a uniform cost search on the sokoban board. Follows the
implementation on pg 84 of AIMA closely.
"""
records = {'node': 0, 'repeat': 0, 'fringe': 0, 'explored': set()}
if board.finished(): # check if initial state is complete
return records, board
board_queue = PriorityCostQueue() # initialize queue
board_queue.push(0, board)
records['node'] += 1
while True:
records['fringe'] = len(board_queue)
if not board_queue: # fail if no options left
print records
raise Exception('Solution not found.')
node_cost, node_board = board_queue.pop()
if node_board.finished(): # return if solved
return records, node_board
records['explored'].add(hash(node_board)) # log board
for direction, cost in node_board.moves_available():
child_board = deepcopy(node_board).move(direction) # copy & move
records['node'] += 1
if hash(child_board) not in records['explored']: # if not explored
if not child_board in board_queue: # if not in queue
board_queue.push(node_cost + cost, child_board)
else: # check if cost can be made lower
board_queue.update_cost(node_cost + cost, child_board)
records['repeat'] += 1
else: # log repeat if already explored
records['repeat'] += 1
def uniform_cost_search(board, print_steps=False):
"""
@param board: a Board object
@param print_steps: flag to print intermediate steps
@return (records, board)
records: a dictionary keeping track of necessary statistics
board: a copy of the board at the finished state.
Contains an array of all moves performed.
Performs a uniform cost search on the sokoban board. Follows the
implementation on pg 84 of AIMA closely.
"""
records = {
'node' : 0,
'repeat' : 0,
'fringe' : 0,
'explored' : set()
}
if board.finished(): # check if initial state is complete
return records, board
board_queue = PriorityCostQueue() # initialize queue
board_queue.push(0, board)
records['node'] += 1
while True:
records['fringe'] = len(board_queue)
if not board_queue: # fail if no options left
print records
raise Exception('Solution not found.')
node_cost, node_board = board_queue.pop()
if node_board.finished(): # return if solved
return records, node_board
records['explored'].add(hash(node_board)) # log board
for direction, cost in node_board.moves_available():
child_board = deepcopy(node_board).move(direction) # copy & move
records['node'] += 1
if hash(child_board) not in records['explored']: # if not explored
if not child_board in board_queue: # if not in queue
board_queue.push(node_cost+cost, child_board)
else: # check if cost can be made lower
board_queue.update_cost(node_cost+cost, child_board)
records['repeat'] += 1
else: # log repeat if already explored
records['repeat'] += 1
def greedy_best_first_search(board, print_steps=False):
"""
@param board: a Board object
@param print_steps: flag to print intermediate steps
@return (records, board)
records: a dictionary keeping track of necessary statistics
board: a copy of the board at the finished state.
Contains an array of all moves performed.
Performs a greedy best first search on the sokoban board.
"""
records = {'node': 0, 'repeat': 0, 'fringe': 0, 'explored': set()}
if board.finished(): # check if initial state is complete
return records, board
board_queue = PriorityCostQueue() # initialize queue
board_queue.push(0, board)
records['node'] += 1
while True:
records['fringe'] = len(board_queue)
if not board_queue: # fail if no options left
print records
raise Exception('Solution not found.')
node_cost, node_board = board_queue.pop()
if node_board.finished(): # return if solved
return records, node_board
records['explored'].add(hash(node_board)) # log board
for direction, cost in node_board.moves_available():
child_board = deepcopy(node_board).move(direction) # copy & move
records['node'] += 1
if hash(child_board) not in records[
'explored'] and child_board not in board_queue:
board_queue.push(child_board.manhattan(), child_board)
else:
records['repeat'] += 1
def greedy_best_first_search(board, print_steps=False):
"""
@param board: a Board object
@param print_steps: flag to print intermediate steps
@return (records, board)
records: a dictionary keeping track of necessary statistics
board: a copy of the board at the finished state.
Contains an array of all moves performed.
Performs a greedy best first search on the sokoban board.
"""
records = {
'node' : 0,
'repeat' : 0,
'fringe' : 0,
'explored' : set()
}
if board.finished(): # check if initial state is complete
return records, board
board_queue = PriorityCostQueue() # initialize queue
board_queue.push(0, board)
records['node'] += 1
while True:
records['fringe'] = len(board_queue)
if not board_queue: # fail if no options left
print records
raise Exception('Solution not found.')
node_cost, node_board = board_queue.pop()
if node_board.finished(): # return if solved
return records, node_board
records['explored'].add(hash(node_board)) # log board
for direction, cost in node_board.moves_available():
child_board = deepcopy(node_board).move(direction) # copy & move
records['node'] += 1
if hash(child_board) not in records['explored'] and child_board not in board_queue:
board_queue.push(child_board.manhattan(), child_board)
else:
records['repeat'] += 1
