simpleai 0.1

An implementation of AI algorithms based on aima-python

Simple AI
=========

(Project home: http://github.com/fisadev/simpleai)

This packages is based and inspired in aima-python:
https://code.google.com/p/aima-python/

We implement all the searches in aima-python plus (LISTAR). Besides, we make
some improvements in terms of efficiency and error handling.

Installation
============

Just get it:

pip install simpleai


Examples
========

Simple AI allows you to define problems and look for the solution with
different strategies. Another samples are in the *samples* directory, but
here is one.

The following problem defines the 8 Puzzle problem (a smaller version of the
Fifteen puzzle: http://en.wikipedia.org/wiki/Fifteen_puzzle). States are
defined as a list of lists of integers, representing the order of the tiles and
0 being the empty tile. The actions are defined as where the empty tile should
go with that action.

After you have the result you can

print result.path()

to get the sequence of actions that lead to the solution from the initial
state.


# coding=utf-8
from simpleai.models import Problem
from simpleai.methods import *
import copy


class EightPuzzleProblem(Problem):

def __init__(self):
initial = [
[0, 2, 3],
[1, 8, 5],
[4, 7, 6]]
super(EightPuzzleProblem, self).__init__(initial_state=initial)

def _get_empty_coordinates(self, state):
'''Returns the coordinates of the empty tile as dictionary'''
for row in state:
if 0 in row:
break
return {'row': state.index(row), 'column': row.index(0)}

def _switch_tiles(self, state, tile1, tile2):
'''Makes a copy of ``state`` and switches the ``tile1`` with
the ``tile2``'''
new_state = copy.deepcopy(state)
aux = new_state[tile2[0]][tile2[1]]
new_state[tile2[0]][tile2[1]] = new_state[tile1[0]][tile1[1]]
new_state[tile1[0]][tile1[1]] = aux
return new_state

def actions(self, state):
actions = []
coordinates = self._get_empty_coordinates(state)
if coordinates['row'] > 0:
actions.append('up')
if coordinates['row'] < 2:
actions.append('down')
if coordinates['column'] > 0:
actions.append('left')
if coordinates['column'] < 2:
actions.append('right')
return actions

def result(self, state, action):
coordinates = self._get_empty_coordinates(state)
empty_tile = (coordinates['row'], coordinates['column'])
if action == 'up' and coordinates['row'] > 0:
tile2 = (coordinates['row'] - 1, coordinates['column'])
if action == 'down' and coordinates['row'] < 2:
tile2 = (coordinates['row'] + 1, coordinates['column'])
if action == 'left' and coordinates['column'] > 0:
tile2 = (coordinates['row'], coordinates['column'] - 1)
if action == 'right' and coordinates['column'] < 2:
tile2 = (coordinates['row'], coordinates['column'] + 1)
new_state = self._switch_tiles(state, empty_tile, tile2)
return new_state

def is_goal(self, state):
''' Check if the state is goal:
| 1 2 3 |
| 4 5 6 |
| 7 8 |
'''
return (state[0] == [1, 2, 3] and
state[1] == [4, 5, 6] and state[2] == [7, 8, 0])

def heuristic(self, state):
total = 0
row_no = 0
for row in state:
for i in row:
total += abs(int((i - 1) / 3) - row_no) + \
abs((i - 1) % 3 - row.index(i))
row_no += 1
return total

problem = EightPuzzleProblem()

result = astar_search(problem)



Authors
=======

Juan Pedro Fisanotti
[e-mail]

Rafael Carrascosa
[e-mail]

Santiago Romero
[e-mail]

Special acknowledgements to Machinalis (http://www.machinalis.com/) for the
time provided to work on this project.