simpleai 0.5.2

An implementation of AI algorithms based on aima-python

Simple AI

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

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 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

  • 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:

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:

from simpleai.search import SearchProblem, astar

GOAL = 'HELLO WORLD'

class HelloProblem(SearchProblem):
    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. Or for offline access, you can clone the project code repository and read them from the docs folder.

Authors

• Juan Pedro Fisanotti <[e-mail]>

• Rafael Carrascosa <[e-mail]>

• Santiago Romero <[e-mail]>

• Gonzalo García Berrotarán <[e-mail]>

Special acknowledgements to Machinalis for the time provided to work on this project.