https://wiki.haskell.org/api.php?action=feedcontributions&user=Zeroskillor&feedformat=atomHaskellWiki - User contributions [en]2024-03-29T14:57:01ZUser contributionsMediaWiki 1.35.5https://wiki.haskell.org/index.php?title=AI&diff=55469AI2013-02-22T01:24:21Z<p>Zeroskillor: /* People */</p>
<hr />
<div>[[Category:Community]]<br />
[[Category:AI]]<br />
== Introduction ==<br />
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.<br />
<br />
== People ==<br />
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.<br />
<br />
Mark Wong-VanHaren (marklar) <markwvh at gmail><br />
<br />
Andrew Wagner (chessguy) <wagner dot andrew at gmail><br />
<br />
Bryan Green (shevek) <dbryan dot green at gmail><br />
<br />
Ricardo Herrmann <rherrmann at gmail><br />
<br />
Dan Doel (dolio) <dan dot doel at gmail><br />
<br />
Chung-chieh Shan (ccshan) <ccshan at cs dot rutgers dot edu><br />
<br />
Adam Wyner (Lawman) <adam dot wyner dot info><br />
<br />
Allan Erskine (thedatabase) <allan dot erskine at gmail><br />
<br />
Dave Tapley (DukeDave) <dukedave at gmail><br />
<br />
Lloyd Allison <lloyd dot allison at infotech dot monash dot edu dot au><br />
<br />
Jim Geovedi <jim at geovedi dot com><br />
<br />
Paul Berg (Procyon) <procyon at procyondevelopments dot com><br />
<br />
Eric Kow (kowey) <eric dot kow at gmail> [watching on the sidelines]<br />
<br />
Charles Blundell <blundellc at gmail><br />
<br />
Mathew Mills (mathewm) <mathewmills (at) gmail (dot) com><br />
<br />
Jason Morton (inverselimit) <jason.morton at gmail><br />
<br />
Jiri Hysek (dvekravy) <xhysek02 at stud dot fit dot vutbr dot cz> [NN, EA]<br />
<br />
Shahbaz Chaudhary <shahbazc at gmail> [interested in GP]<br />
<br />
Hans van Thiel <hthiel dot char á zonnet tot nl> [automated rule discovery, author of the Emping data mining utility]<br />
<br />
Alp Mestanogullari (Alpounet) <alp (at) mestan (dot) fr> [machine learning mainly]<br />
<br />
Chris Pettitt (cpettitt) <cpettitt at gmail><br />
<br />
Nathaniel Neitzke (nneitzke) <nightski at gmail><br />
<br />
Ricardo Honorato-Zimmer (_rata_) <rikardo dot horo at gmail dot com><br />
<br />
Raphael Javaux (RaphaelJ) <raphaeljavaux at gmail dot com><br />
<br />
Mahmut Bulut (vertexclique) <mahmutbulut0 at gmail dot com> (ML, natural language processing, swarming intelligence)<br />
<br />
Mike Izbicki <mike at izbicki.me><br />
<br />
Chris Taylor (crntaylor) <crntaylor at gmail><br />
<br />
Libor Wagner <wagnelib at cmp dot felk dot cvut dot cz><br />
<br />
Florian Grunert <fgrunert ätt uni-osnabrueck dot de><br />
<br />
== Ideas ==<br />
<br />
* 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, [http://conway.rutgers.edu/~ccshan/wiki/cs504/posts/Second_week.html as I've started to explore as part of a course on computational modeling that I'm teaching]. Given that [http://www.cs.yale.edu/homes/hudak-paul/hudak-dir/ACM-WS/position.html 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 [http://ai.stanford.edu/~shoham/www%20papers/RatProg.pdf rational programming language] like [http://www.eecs.harvard.edu/~avi/ Avi Pfeffer]'s [http://www.eecs.harvard.edu/~avi/IBAL/index.html IBAL] -- not only [http://www.eecs.harvard.edu/~nr/pubs/pmonad-abstract.html is probability distribution a monad], I just realized that [http://ttic.uchicago.edu/~dmcallester/bayes.ps a certain kind of variable elimination] is simply garbage collection in a call-by-need language!<br />
<br />
== Things that need a home ==<br />
<br />
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:<br />
* http://catenova.org/~awagner/Simplifier (broken link)<br />
**This was given to me by Alfonso Acosta (mentioned recently on haskell-cafe)<br />
<br />
*http://catenova.org/~awagner/GPLib (broken link)<br />
**[[GPLib]] is a work in progress by yours truly, hopefully a future framework for genetic algorithms in haskell.<br />
<br />
*http://www.haskell.org/haskellwiki/Libraries_and_tools/Linguistics<br />
<br />
I've proposed a machine learning library for this year's Google Summer of Code. [http://hackage.haskell.org/trac/summer-of-code/ticket/1127] 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>. --[[User:Ketil|Ketil]] 07:46, 26 March 2007 (UTC)<br />
<br />
Martin Erwig's probabilistic functional programming (PFP) project, including an implementation of the probability monad:<br />
*http://web.engr.oregonstate.edu/~erwig/pfp/<br />
<br />
Culmination of some recent posts about the probability monad on Random Hacks (including a darcs repository):<br />
*http://www.randomhacks.net/articles/2007/03/03/smart-classification-with-haskell <br />
<br />
sigfpe's coverage and highly algebraic view of the probability monad in Haskell:<br />
*http://sigfpe.blogspot.com/2007/02/monads-for-vector-spaces-probability.html<br />
<br />
Two links I found today that are interesting:<br />
*http://perception.inf.um.es/darcs/darcsweb.cgi<br />
*http://www-student.cs.york.ac.uk/~cb224/<br />
<br />
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?<br />
*http://www.cs.chalmers.se/~patrikj/poly/unify/<br />
<br />
Easy-to-use work-in-progress neural network library, by [[User:AlpMestan|Alp Mestan]] and Chaddaï Fouché :<br />
*http://github.com/alpmestan/HNN/tree/master<br />
<br />
Implementation of some of the algorithms in Russell and Norvig's "Artificial Intelligence: A Modern Approach", by [[User:Crntaylor|Chris Taylor]]:<br />
*https://github.com/chris-taylor/aima-haskell<br />
<br />
== Proposed Module Hierarchy ==<br />
*AI<br />
**AI.Searching<br />
***AI.Searching.Evolutionary<br />
**AI.Logic<br />
**AI.Planning<br />
***AI.Planning.Swarm<br />
**AI.Probabilistic<br />
**AI.Learning<br />
***AI.Learning.Kernel<br />
***AI.Learning.NeuralNet<br />
**AI.Classification<br />
***AI.Classification.ExpertSystem<br />
**AI.Communication<br />
<br />
==Proposed sample format for a wiki page on a topic or sub-topic==<br />
<br />
'''AI/Logic/Fuzzy'''<br />
<br />
The slashes show that [[/Logic | Logic]] is a subpage of [[AI]] and [[/Logic/Fuzzy | Fuzzy]] is a subpage of [[AI/Logic]]. MediaWiki will then generate links back up the chain of pages. (Try the links to see)<br />
<br />
*Fuzzy logic is blah blah...<br />
*Sub-topics:<br />
**Trivial fuzzy logic in Haskell<br />
**Type 2 fuzzy logic<br />
*Links to existing literature:<br />
**General<br />
***My first fuzzy logic book<br />
**Specific to functional programming / Haskell<br />
***Fun with fuzzy functions<br />
*Typical problems:<br />
**Problem 1: blah blah blah<br />
**Problem 2: blah blah blah<br />
*List of people involved in the area<br />
** Me<br />
**Someone else<br />
*Body<br />
**List of goals<br />
**Progress being made on them<br />
**Code and documentation.<br />
<br />
==Current sub-pages==<br />
*[[/Logic/Fuzzy]]<br />
*[[/Genetic programming/Evolutionary chess]]<br />
*[[/Genetic programming/GPLib]]<br />
<br />
<br />
== External links ==<br />
<br />
* [http://hackage.haskell.org/packages/archive/pkg-list.html#cat:ai Packages at Hackage, marked AI]<br />
* [https://patch-tag.com/r/alpmestan/hasklab/wiki/ HaskLab Wiki]<br />
* [http://projects.haskell.org/cgi-bin/mailman/listinfo/hasklab The HaskLab mailing-list]<br />
* [http://projects.haskell.org/pipermail/hasklab/ The HaskLab Archives] (mailing-list archive)<br />
* [http://jpmoresmau.blogspot.com/2010/09/digit-recognition-with-neural-network.html Digit recognition with a neural network. First attempt!] (Blog article)<br />
* [http://jpmoresmau.blogspot.com/2010/09/haskell-neural-network-plugging-space.html Haskell Neural Network: plugging a space leak] (Blog article)<br />
* [http://www.ki.informatik.uni-frankfurt.de/research/HCAR.html Further Reading]<br />
* [https://github.com/smichal/hs-logic hs-logic]; logic programming in Haskell (software on github)</div>Zeroskillorhttps://wiki.haskell.org/index.php?title=AI&diff=55463AI2013-02-19T22:00:31Z<p>Zeroskillor: /* People */</p>
<hr />
<div>[[Category:Community]]<br />
[[Category:AI]]<br />
== Introduction ==<br />
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.<br />
<br />
== People ==<br />
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.<br />
<br />
Mark Wong-VanHaren (marklar) <markwvh at gmail><br />
<br />
Andrew Wagner (chessguy) <wagner dot andrew at gmail><br />
<br />
Bryan Green (shevek) <dbryan dot green at gmail><br />
<br />
Ricardo Herrmann <rherrmann at gmail><br />
<br />
Dan Doel (dolio) <dan dot doel at gmail><br />
<br />
Chung-chieh Shan (ccshan) <ccshan at cs dot rutgers dot edu><br />
<br />
Adam Wyner (Lawman) <adam dot wyner dot info><br />
<br />
Allan Erskine (thedatabase) <allan dot erskine at gmail><br />
<br />
Dave Tapley (DukeDave) <dukedave at gmail><br />
<br />
Lloyd Allison <lloyd dot allison at infotech dot monash dot edu dot au><br />
<br />
Jim Geovedi <jim at geovedi dot com><br />
<br />
Paul Berg (Procyon) <procyon at procyondevelopments dot com><br />
<br />
Eric Kow (kowey) <eric dot kow at gmail> [watching on the sidelines]<br />
<br />
Charles Blundell <blundellc at gmail><br />
<br />
Mathew Mills (mathewm) <mathewmills (at) gmail (dot) com><br />
<br />
Jason Morton (inverselimit) <jason.morton at gmail><br />
<br />
Jiri Hysek (dvekravy) <xhysek02 at stud dot fit dot vutbr dot cz> [NN, EA]<br />
<br />
Shahbaz Chaudhary <shahbazc at gmail> [interested in GP]<br />
<br />
Hans van Thiel <hthiel dot char á zonnet tot nl> [automated rule discovery, author of the Emping data mining utility]<br />
<br />
Alp Mestanogullari (Alpounet) <alp (at) mestan (dot) fr> [machine learning mainly]<br />
<br />
Chris Pettitt (cpettitt) <cpettitt at gmail><br />
<br />
Nathaniel Neitzke (nneitzke) <nightski at gmail><br />
<br />
Ricardo Honorato-Zimmer (_rata_) <rikardo dot horo at gmail dot com><br />
<br />
Raphael Javaux (RaphaelJ) <raphaeljavaux at gmail dot com><br />
<br />
Mahmut Bulut (vertexclique) <mahmutbulut0 at gmail dot com> (ML, natural language processing, swarming intelligence)<br />
<br />
Mike Izbicki <mike at izbicki.me><br />
<br />
Chris Taylor (crntaylor) <crntaylor at gmail><br />
<br />
Libor Wagner <wagnelib at cmp dot felk dot cvut dot cz><br />
<br />
Florian Grunert <fgrunert ätt uni-osnabrueck dot org><br />
<br />
== Ideas ==<br />
<br />
* 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, [http://conway.rutgers.edu/~ccshan/wiki/cs504/posts/Second_week.html as I've started to explore as part of a course on computational modeling that I'm teaching]. Given that [http://www.cs.yale.edu/homes/hudak-paul/hudak-dir/ACM-WS/position.html 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 [http://ai.stanford.edu/~shoham/www%20papers/RatProg.pdf rational programming language] like [http://www.eecs.harvard.edu/~avi/ Avi Pfeffer]'s [http://www.eecs.harvard.edu/~avi/IBAL/index.html IBAL] -- not only [http://www.eecs.harvard.edu/~nr/pubs/pmonad-abstract.html is probability distribution a monad], I just realized that [http://ttic.uchicago.edu/~dmcallester/bayes.ps a certain kind of variable elimination] is simply garbage collection in a call-by-need language!<br />
<br />
== Things that need a home ==<br />
<br />
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:<br />
* http://catenova.org/~awagner/Simplifier (broken link)<br />
**This was given to me by Alfonso Acosta (mentioned recently on haskell-cafe)<br />
<br />
*http://catenova.org/~awagner/GPLib (broken link)<br />
**[[GPLib]] is a work in progress by yours truly, hopefully a future framework for genetic algorithms in haskell.<br />
<br />
*http://www.haskell.org/haskellwiki/Libraries_and_tools/Linguistics<br />
<br />
I've proposed a machine learning library for this year's Google Summer of Code. [http://hackage.haskell.org/trac/summer-of-code/ticket/1127] 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>. --[[User:Ketil|Ketil]] 07:46, 26 March 2007 (UTC)<br />
<br />
Martin Erwig's probabilistic functional programming (PFP) project, including an implementation of the probability monad:<br />
*http://web.engr.oregonstate.edu/~erwig/pfp/<br />
<br />
Culmination of some recent posts about the probability monad on Random Hacks (including a darcs repository):<br />
*http://www.randomhacks.net/articles/2007/03/03/smart-classification-with-haskell <br />
<br />
sigfpe's coverage and highly algebraic view of the probability monad in Haskell:<br />
*http://sigfpe.blogspot.com/2007/02/monads-for-vector-spaces-probability.html<br />
<br />
Two links I found today that are interesting:<br />
*http://perception.inf.um.es/darcs/darcsweb.cgi<br />
*http://www-student.cs.york.ac.uk/~cb224/<br />
<br />
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?<br />
*http://www.cs.chalmers.se/~patrikj/poly/unify/<br />
<br />
Easy-to-use work-in-progress neural network library, by [[User:AlpMestan|Alp Mestan]] and Chaddaï Fouché :<br />
*http://github.com/alpmestan/HNN/tree/master<br />
<br />
Implementation of some of the algorithms in Russell and Norvig's "Artificial Intelligence: A Modern Approach", by [[User:Crntaylor|Chris Taylor]]:<br />
*https://github.com/chris-taylor/aima-haskell<br />
<br />
== Proposed Module Hierarchy ==<br />
*AI<br />
**AI.Searching<br />
***AI.Searching.Evolutionary<br />
**AI.Logic<br />
**AI.Planning<br />
***AI.Planning.Swarm<br />
**AI.Probabilistic<br />
**AI.Learning<br />
***AI.Learning.Kernel<br />
***AI.Learning.NeuralNet<br />
**AI.Classification<br />
***AI.Classification.ExpertSystem<br />
**AI.Communication<br />
<br />
==Proposed sample format for a wiki page on a topic or sub-topic==<br />
<br />
'''AI/Logic/Fuzzy'''<br />
<br />
The slashes show that [[/Logic | Logic]] is a subpage of [[AI]] and [[/Logic/Fuzzy | Fuzzy]] is a subpage of [[AI/Logic]]. MediaWiki will then generate links back up the chain of pages. (Try the links to see)<br />
<br />
*Fuzzy logic is blah blah...<br />
*Sub-topics:<br />
**Trivial fuzzy logic in Haskell<br />
**Type 2 fuzzy logic<br />
*Links to existing literature:<br />
**General<br />
***My first fuzzy logic book<br />
**Specific to functional programming / Haskell<br />
***Fun with fuzzy functions<br />
*Typical problems:<br />
**Problem 1: blah blah blah<br />
**Problem 2: blah blah blah<br />
*List of people involved in the area<br />
** Me<br />
**Someone else<br />
*Body<br />
**List of goals<br />
**Progress being made on them<br />
**Code and documentation.<br />
<br />
==Current sub-pages==<br />
*[[/Logic/Fuzzy]]<br />
*[[/Genetic programming/Evolutionary chess]]<br />
*[[/Genetic programming/GPLib]]<br />
<br />
<br />
== External links ==<br />
<br />
* [http://hackage.haskell.org/packages/archive/pkg-list.html#cat:ai Packages at Hackage, marked AI]<br />
* [https://patch-tag.com/r/alpmestan/hasklab/wiki/ HaskLab Wiki]<br />
* [http://projects.haskell.org/cgi-bin/mailman/listinfo/hasklab The HaskLab mailing-list]<br />
* [http://projects.haskell.org/pipermail/hasklab/ The HaskLab Archives] (mailing-list archive)<br />
* [http://jpmoresmau.blogspot.com/2010/09/digit-recognition-with-neural-network.html Digit recognition with a neural network. First attempt!] (Blog article)<br />
* [http://jpmoresmau.blogspot.com/2010/09/haskell-neural-network-plugging-space.html Haskell Neural Network: plugging a space leak] (Blog article)<br />
* [http://www.ki.informatik.uni-frankfurt.de/research/HCAR.html Further Reading]<br />
* [https://github.com/smichal/hs-logic hs-logic]; logic programming in Haskell (software on github)</div>Zeroskillorhttps://wiki.haskell.org/index.php?title=AI&diff=55462AI2013-02-19T21:58:46Z<p>Zeroskillor: /* People */</p>
<hr />
<div>[[Category:Community]]<br />
[[Category:AI]]<br />
== Introduction ==<br />
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.<br />
<br />
== People ==<br />
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.<br />
<br />
Mark Wong-VanHaren (marklar) <markwvh at gmail><br />
<br />
Andrew Wagner (chessguy) <wagner dot andrew at gmail><br />
<br />
Bryan Green (shevek) <dbryan dot green at gmail><br />
<br />
Ricardo Herrmann <rherrmann at gmail><br />
<br />
Dan Doel (dolio) <dan dot doel at gmail><br />
<br />
Chung-chieh Shan (ccshan) <ccshan at cs dot rutgers dot edu><br />
<br />
Adam Wyner (Lawman) <adam dot wyner dot info><br />
<br />
Allan Erskine (thedatabase) <allan dot erskine at gmail><br />
<br />
Dave Tapley (DukeDave) <dukedave at gmail><br />
<br />
Lloyd Allison <lloyd dot allison at infotech dot monash dot edu dot au><br />
<br />
Jim Geovedi <jim at geovedi dot com><br />
<br />
Paul Berg (Procyon) <procyon at procyondevelopments dot com><br />
<br />
Eric Kow (kowey) <eric dot kow at gmail> [watching on the sidelines]<br />
<br />
Charles Blundell <blundellc at gmail><br />
<br />
Mathew Mills (mathewm) <mathewmills (at) gmail (dot) com><br />
<br />
Jason Morton (inverselimit) <jason.morton at gmail><br />
<br />
Jiri Hysek (dvekravy) <xhysek02 at stud dot fit dot vutbr dot cz> [NN, EA]<br />
<br />
Shahbaz Chaudhary <shahbazc at gmail> [interested in GP]<br />
<br />
Hans van Thiel <hthiel dot char á zonnet tot nl> [automated rule discovery, author of the Emping data mining utility]<br />
<br />
Alp Mestanogullari (Alpounet) <alp (at) mestan (dot) fr> [machine learning mainly]<br />
<br />
Chris Pettitt (cpettitt) <cpettitt at gmail><br />
<br />
Nathaniel Neitzke (nneitzke) <nightski at gmail><br />
<br />
Ricardo Honorato-Zimmer (_rata_) <rikardo dot horo at gmail dot com><br />
<br />
Raphael Javaux (RaphaelJ) <raphaeljavaux at gmail dot com><br />
<br />
Mahmut Bulut (vertexclique) <mahmutbulut0 at gmail dot com> (ML, natural language processing, swarming intelligence)<br />
<br />
Mike Izbicki <mike at izbicki.me><br />
<br />
Chris Taylor (crntaylor) <crntaylor at gmail><br />
<br />
Libor Wagner <wagnelib at cmp dot felk dot cvut dot cz><br />
<br />
Florian Grunert <zeroskillor ätt zeroskillor dot org><br />
<br />
== Ideas ==<br />
<br />
* 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, [http://conway.rutgers.edu/~ccshan/wiki/cs504/posts/Second_week.html as I've started to explore as part of a course on computational modeling that I'm teaching]. Given that [http://www.cs.yale.edu/homes/hudak-paul/hudak-dir/ACM-WS/position.html 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 [http://ai.stanford.edu/~shoham/www%20papers/RatProg.pdf rational programming language] like [http://www.eecs.harvard.edu/~avi/ Avi Pfeffer]'s [http://www.eecs.harvard.edu/~avi/IBAL/index.html IBAL] -- not only [http://www.eecs.harvard.edu/~nr/pubs/pmonad-abstract.html is probability distribution a monad], I just realized that [http://ttic.uchicago.edu/~dmcallester/bayes.ps a certain kind of variable elimination] is simply garbage collection in a call-by-need language!<br />
<br />
== Things that need a home ==<br />
<br />
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:<br />
* http://catenova.org/~awagner/Simplifier (broken link)<br />
**This was given to me by Alfonso Acosta (mentioned recently on haskell-cafe)<br />
<br />
*http://catenova.org/~awagner/GPLib (broken link)<br />
**[[GPLib]] is a work in progress by yours truly, hopefully a future framework for genetic algorithms in haskell.<br />
<br />
*http://www.haskell.org/haskellwiki/Libraries_and_tools/Linguistics<br />
<br />
I've proposed a machine learning library for this year's Google Summer of Code. [http://hackage.haskell.org/trac/summer-of-code/ticket/1127] 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>. --[[User:Ketil|Ketil]] 07:46, 26 March 2007 (UTC)<br />
<br />
Martin Erwig's probabilistic functional programming (PFP) project, including an implementation of the probability monad:<br />
*http://web.engr.oregonstate.edu/~erwig/pfp/<br />
<br />
Culmination of some recent posts about the probability monad on Random Hacks (including a darcs repository):<br />
*http://www.randomhacks.net/articles/2007/03/03/smart-classification-with-haskell <br />
<br />
sigfpe's coverage and highly algebraic view of the probability monad in Haskell:<br />
*http://sigfpe.blogspot.com/2007/02/monads-for-vector-spaces-probability.html<br />
<br />
Two links I found today that are interesting:<br />
*http://perception.inf.um.es/darcs/darcsweb.cgi<br />
*http://www-student.cs.york.ac.uk/~cb224/<br />
<br />
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?<br />
*http://www.cs.chalmers.se/~patrikj/poly/unify/<br />
<br />
Easy-to-use work-in-progress neural network library, by [[User:AlpMestan|Alp Mestan]] and Chaddaï Fouché :<br />
*http://github.com/alpmestan/HNN/tree/master<br />
<br />
Implementation of some of the algorithms in Russell and Norvig's "Artificial Intelligence: A Modern Approach", by [[User:Crntaylor|Chris Taylor]]:<br />
*https://github.com/chris-taylor/aima-haskell<br />
<br />
== Proposed Module Hierarchy ==<br />
*AI<br />
**AI.Searching<br />
***AI.Searching.Evolutionary<br />
**AI.Logic<br />
**AI.Planning<br />
***AI.Planning.Swarm<br />
**AI.Probabilistic<br />
**AI.Learning<br />
***AI.Learning.Kernel<br />
***AI.Learning.NeuralNet<br />
**AI.Classification<br />
***AI.Classification.ExpertSystem<br />
**AI.Communication<br />
<br />
==Proposed sample format for a wiki page on a topic or sub-topic==<br />
<br />
'''AI/Logic/Fuzzy'''<br />
<br />
The slashes show that [[/Logic | Logic]] is a subpage of [[AI]] and [[/Logic/Fuzzy | Fuzzy]] is a subpage of [[AI/Logic]]. MediaWiki will then generate links back up the chain of pages. (Try the links to see)<br />
<br />
*Fuzzy logic is blah blah...<br />
*Sub-topics:<br />
**Trivial fuzzy logic in Haskell<br />
**Type 2 fuzzy logic<br />
*Links to existing literature:<br />
**General<br />
***My first fuzzy logic book<br />
**Specific to functional programming / Haskell<br />
***Fun with fuzzy functions<br />
*Typical problems:<br />
**Problem 1: blah blah blah<br />
**Problem 2: blah blah blah<br />
*List of people involved in the area<br />
** Me<br />
**Someone else<br />
*Body<br />
**List of goals<br />
**Progress being made on them<br />
**Code and documentation.<br />
<br />
==Current sub-pages==<br />
*[[/Logic/Fuzzy]]<br />
*[[/Genetic programming/Evolutionary chess]]<br />
*[[/Genetic programming/GPLib]]<br />
<br />
<br />
== External links ==<br />
<br />
* [http://hackage.haskell.org/packages/archive/pkg-list.html#cat:ai Packages at Hackage, marked AI]<br />
* [https://patch-tag.com/r/alpmestan/hasklab/wiki/ HaskLab Wiki]<br />
* [http://projects.haskell.org/cgi-bin/mailman/listinfo/hasklab The HaskLab mailing-list]<br />
* [http://projects.haskell.org/pipermail/hasklab/ The HaskLab Archives] (mailing-list archive)<br />
* [http://jpmoresmau.blogspot.com/2010/09/digit-recognition-with-neural-network.html Digit recognition with a neural network. First attempt!] (Blog article)<br />
* [http://jpmoresmau.blogspot.com/2010/09/haskell-neural-network-plugging-space.html Haskell Neural Network: plugging a space leak] (Blog article)<br />
* [http://www.ki.informatik.uni-frankfurt.de/research/HCAR.html Further Reading]<br />
* [https://github.com/smichal/hs-logic hs-logic]; logic programming in Haskell (software on github)</div>Zeroskillor