Herself’s Artificial Intelligence

Stanford School of Engineering, AI/Machine Learning

Video Lectures.net has flash formatted video lectures. Right now there are many on computer science including Data Mining, Data Visualization, Fuzzy Logic, Image Analysis, Information Extraction, Information Retrieval, Machine Learning ( 326 lectures! ), Robotics, The Semantic Web and Text Mining.

Or you can try one of the many MIT Computer Courses These usually list the text book, have a syllabus, lecture notes, sometimes videos and homework and test problems and solutions.

Also there is MIT’s Introduction to Neural Networks

And Introduction to Machine Learning a PDF textbook draft you can download.

There are also many documents on Machine Learning at Scribd.

UCI Machine Learning Repository has over 160 data sets for you to use to test and develop your AI.

I ran across this website, Computer Programming Algorithms Directory, while looking for resources for topics on this website. It’s not very large but it’s a good collection of algorithms and source code many of which will be of interest to artificial intelligence developers.

If you are looking for quick how to do somethings use Google Code Search. I find it useful for looking up specific things I know how to do but have forgotten the details.

There is also Sourcebank which has a directory of links to source code.

And for bigger projects, many of which you can join and contribute to try Source Forge

And there’s a list to your right of source code on various topics on this site. Topics that don’t have source code here usually have the algorithms spelled out for you. See specific topics for more information.

Designing AI Algorithms for Turn-Based Strategy Games

In action games the AI opponent always has the natural advantage: perfect accuracy and lightning fast reflexes, so the challenge in designing the AI for those games is making it act more human and to be beatable.

In turn-based strategy games the tables are turned. Speed and accuracy are no longer important factors and the cunning and intuition of the human player will easily out match any AI opponent. In fact, it’s nearly impossible to design a AI that can beat an experienced player, but that is not really the point anyway.

The challenge is to make the AI’s attack and defense strategy to appear intelligent and thought out, providing a challenge but letting the player win in the end. Once the player has familiarized himself/herself with the tactics of the AI the game rapidly gets boring, so a certain amount of unpredictability is desirable. . . .

I ran across this article while looking into predator prey chases and thought it might be of interest to some of you.

See also:
Simple predator prey chase algorithms
Predator prey chases using potential functions

If you are in school you should grab one of these packages at the student price:
Mathematica
Maple soft
MathLab

If you have a few dollars
MuPAD is extremely well priced compared to Mathematica, Maple and MathLab.

Following are free and open source versions for those of us no longer getting student discounts.
Sage – installs, extends and provides a front end for Maxima ( It’s a great way to get Maxima installed and running easily on your computer, versions for Win/Linux/OSX )
Maxima has native Windows and Linux Code. ( Instructions for getting it up and running on OS X )
SciLab There are downloads for Windows and Linux and a link to an OSX version.
Axiom has Linux and Windows versions and
OS X instructions
Yacas should word on all operating systems.
Octave has OSX, Linux and Windows versions.
Euler has Linux and Windows versions. It should work on OS X as well.
A link list of other open source computer algebra programs including Jasymca so you can derive and integrate on your mobile smart phone.

Because these software packages are open source you should be able to download and compile any of them for your computer if a binary version is not available.

See also on this site:Useful math websites

Talking Robots Podcast from the Laboratory of Intelligent Systems features interviews with leading researchers in robotics.

The podcast is done Dario Floreano a professor at Ecole Polytechnique Federale de Lausanne in France. His main interests are in evolutionary robotics. He has also done significant work in neural systems, artificial life, evolutionary computation, swarm robotics and biomimetic electronics.