An implementation of AI algorithms based on aima-python

Project description

Simple AI
=========

This lib implements many of the artificial intelligence algorithms described on the book "Artificial Ingelligence, a Modern Approach", from Stuart Russel and Peter Norvig. We strongly recommend you to read the book, or at least the introductory chapters and the ones related to the components you want to use, because we won't explain the algorithms here.

This implementation takes some of the ideas from the Norvig's implementation (the `aima-python <https://code.google.com/p/aima-python/>`_ lib), but it's made with a more "pythonic" approach, and more enphasis on creating a stable, modern, and mantenible version. We are testing the majority of the lib, it's available via pip install, has a standar repo and lib architecture, well documented, respects the python pep8 guidelines, provides only working code (no placeholders for future things), etc. Even the internal code is written with readability in mind, not only the external API.

At this moment, the implementation includes:

* Search algorithms (not informed and informed)
* Local Search algorithms
* Constraint Satisfaction Problems algorithms

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

Just get it:

.. code-block:: none

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 an easy one.

This problem tries to create the string "HELLO WORLD" using the A* algorithm:

.. code-block:: python

from simpleai.models import Problem
from simpleai.search import astar

GOAL = 'HELLO WORLD'

class HelloProblem(Problem):
def actions(self, state):
if len(state) < len(GOAL):
return [c for c in ' ABCDEFGHIJKLMNOPQRSTUVWXYZ']
else:
return []

def result(self, state, action):
return state + action

def is_goal(self, state):
return state == GOAL

def heuristic(self, state):
# how far are we from the goal?
wrong = sum([1 if state[i] != GOAL[i] else 0
for i in range(len(state))])
missing = len(GOAL) - len(state)
return wrong + missing

problem = HelloProblem(initial_state='')
result = astar(problem)

print result.state
print result.path()

More detailed documentation
===========================

You can read the docs online `here <http://simpleai.readthedocs.org/en/latest/>`_. Or for offline access, you can clone the project code repository and read them from the ``docs`` folder.

Authors
=======

* Rafael Carrascosa <rcarrascosa@machinalis.com>
* Santiago Romero <sromero@machinalis.com>

Special acknowledgements to `Machinalis <http://www.machinalis.com/>`_ for the
time provided to work on this project.