This is the home for the Haskell AI Strike Force! Here we will collect code, problems, papers, ideas, and people for putting together a flexible AI toolkit in Haskell.
If interested in contributing to or monitoring this project, please put your name, nickname (if applicable - e.g., if you talk on #haskell), and email address so we can keep each other up-to-date.
Andrew Wagner (chessguy) <wagner dot andrew at gmail>
Bryan Green (shevek) <dbryan dot green at gmail>
Ricardo Herrmann <rherrmann at gmail>
Dan Doel (dolio) <dan dot doel at gmail>
Chung-chieh Shan (ccshan) <ccshan at cs dot rutgers dot edu>
Adam Wyner (Lawman) <adam dot wyner dot info>
Allan Erskine (thedatabase) <allan dot erskine at gmail>
Dave Tapley (DukeDave) <dukedave at gmail>
Lloyd Allison <lloyd dot allison at infotech dot monash dot edu dot au>
Paul Berg (Procyon) <procyon at procyondevelopments dot com>
Eric Kow (kowey) <eric dot kow at gmail> [watching on the sidelines]
Charles Blundell <blundellc at gmail>
David Amos <polyomino (at) f2s (dot) com>
Mathew Mills (mathewm) <mathewmills (at) gmail (dot) com>
Jason Morton (inverselimit) <jason.morton at gmail>
Jiri Hysek (dvekravy) <xhysek02 at stud dot fit dot vutbr dot cz>
Shahbaz Chaudhary <shahbazc at gmail> [interested in GP]
- In short, parts of this project can range from established ideas to new syntheses. ccshan: The high level of domain-specific abstraction that Haskell enables is ideal for AI, because AI programs are often "meta": we need to model agents who model the world, and sometimes to model agents who model agents who model the world, etc. In particular, monads are a good way to structure and solve decision processes, as I've started to explore as part of a course on computational modeling that I'm teaching. Given that Haskell is a good language for modular interpreters and compilers, it would also be nice to create and refactor in Haskell an implementation of a rational programming language like Avi Pfeffer's IBAL -- not only is probability distribution a monad, I just realized that a certain kind of variable elimination is simply garbage collection in a call-by-need language!
Things that need a home
If there are things that should be included in the project, but you're not sure where it should go, place it here! I'll start with:
- This was given to me by Alfonso Acosta (mentioned recently on haskell-cafe)
- GPLib is a work in progress by yours truly, hopefully a future framework for genetic algorithms in haskell.
I've proposed a machine learning library for this year's Google Summer of Code.  There has been a few interested (and seemingly well qualified) students, too. I'm not sure if it qualifes as "AI", but if you are interested in this project (as a potential student, mentor, or just...well, interested), please add yourself to the above link, and/or get in touch with me at <ketil at malde dot org>. --Ketil 07:46, 26 March 2007 (UTC)
Martin Erwig's probabilistic functional programming (PFP) project, including an implementation of the probability monad:
Culmination of some recent posts about the probability monad on Random Hacks (including a darcs repository):
sigfpe's coverage and highly algebraic view of the probability monad in Haskell:
Two links I found today that are interesting:
Polytypic unification - unification seems particularly useful for AI tasks (at least natural language stuff)... wouldn't be nice to have a generic library that does it for you?
Proposed Module Hierarchy:
Proposed Sample Format for a wiki page on a topic or sub-topic
- Fuzzy logic is blah blah...
- Trivial fuzzy logic in Haskell
- Type 2 fuzzy logic
- Links to existing literature:
- My first fuzzy logic book
- Specific to functional programming / Haskell
- Fun with fuzzy functions
- Typical problems:
- Problem 1: blah blah blah
- Problem 2: blah blah blah
- List of people involved in the area
- Someone else
- List of goals
- Progress being made on them
- Code and documentation.