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 emphasis 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).
Just get it:
pip install simpleai
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()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.
- Juan Pedro Fisanotti <[email protected]>
- Rafael Carrascosa <[email protected]>
- Santiago Romero <[email protected]>
- Gonzalo García Berrotarán <[email protected]>
Special acknowledgements to Machinalis for the time provided to work on this project.