# Difference between revisions of "AI"

(added a link to the WIP neural network library) |
m (changed link to HNN) |
||

Line 76: | Line 76: | ||

Easy-to-use work-in-progress neural network library, by [[User:AlpMestan|Alp Mestan]] and Chaddaï Fouché : |
Easy-to-use work-in-progress neural network library, by [[User:AlpMestan|Alp Mestan]] and Chaddaï Fouché : |
||

− | *http:// |
+ | *http://github.com/alpmestan/HNN/tree/master |

== Proposed Module Hierarchy == |
== Proposed Module Hierarchy == |

## Revision as of 18:12, 28 August 2009

## Contents

## Introduction

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.

## People

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>

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> [NN, EA]

Shahbaz Chaudhary <shahbazc at gmail> [interested in GP]

Hans van Thiel <hthiel dot char á zonnet tot nl> [automated rule discovery, author of the Emping data mining utility]

Alp Mestan (Alpounet) <alp (at) mestan (dot) fr> [machine learning mainly]

## Ideas

- 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:

- http://catenova.org/~awagner/Simplifier
- This was given to me by Alfonso Acosta (mentioned recently on haskell-cafe)

- http://catenova.org/~awagner/GPLib
- 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. [1] 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?

Easy-to-use work-in-progress neural network library, by Alp Mestan and Chaddaï Fouché :

## Proposed Module Hierarchy

- AI
- AI.Searching
- AI.Searching.Evolutionary

- AI.Logic
- AI.Planning
- AI.Probabilistic
- AI.Learning
- AI.Learning.Kernel
- AI.Learning.NeuralNet

- AI.Classification
- AI.Classification.ExpertSystem

- AI.Communication

- AI.Searching

## Proposed sample format for a wiki page on a topic or sub-topic

**AI/Logic/Fuzzy**

The slashes show that Logic is a subpage of **AI** and Fuzzy is a subpage of AI/Logic. MediaWiki will then generate links back up the chain of pages. (Try the links to see)

- Fuzzy logic is blah blah...
- Sub-topics:
- Trivial fuzzy logic in Haskell
- Type 2 fuzzy logic

- Links to existing literature:
- General
- My first fuzzy logic book

- Specific to functional programming / Haskell
- Fun with fuzzy functions

- General
- Typical problems:
- Problem 1: blah blah blah
- Problem 2: blah blah blah

- List of people involved in the area
- Me
- Someone else

- Body
- List of goals
- Progress being made on them
- Code and documentation.