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An implementation of AI algorithms based on aima-python

Project Description

Simple AI
=========

Project home: http://github.com/simpleai-team/simpleai

This lib implements many of the artificial intelligence algorithms described on the book "Artificial Intelligence, 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 emphasis on creating a stable, modern, and maintainable version. We are testing the majority of the lib, it's available via pip install, has a standard 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
* Traditional search algorithms (not informed and informed)
* Local Search algorithms
* Constraint Satisfaction Problems algorithms
* Machine Learning
* Statistical Classification

And we are working on an interactive execution viewer for search algorithms (display the search tree on each iteration).


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.search import SearchProblem, astar

GOAL = 'HELLO WORLD'

class HelloProblem(SearchProblem):
def actions(self, state):
if len(state) < len(GOAL):
return list(' 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.

Help and discussion
===================

Join us at the Simple AI `google group <http://groups.google.com/group/simpleai>`_.


Authors
=======

* Juan Pedro Fisanotti <fisadev@gmail.com>
* Rafael Carrascosa <rcarrascosa@machinalis.com>
* Santiago Romero <sromero@machinalis.com>
* Gonzalo García Berrotarán <ggarcia@machinalis.com>
* Special acknowledgements to `Machinalis <http://www.machinalis.com/>`_ for the time provided to work on this project. Machinalis also works on some other very interesting projects, like `Quepy <http://quepy.machinalis.com/>`_ and `more <https://github.com/machinalis>`_.

Release history Release notifications

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0.8.1

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0.5.10

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