Neural Networks:

• An Introduction to Neural Networks
• Neural Networks Tutorial
• Back-Propagation Tutorial
• Neural Networks by Ingrid Russel
• Neural Nets by Kevin Gurney

  Evolutionary                Computation:  

• ISGEC
• International Society for Genetic and EC
• Introduction to Evolutionary Computing
• Genetic Algorithms Tutorial
• Genetic Algorithms and Evolutionary Computing

  Journals and Magazines:

• AI magazine
• IEEE Intelligent Systems*
• IEEE Expert*

* Access through ALADIN

  AI Resources and Links:

• AAAI
• Introduction to AI

  Groups & Communities:

• Google AI

  Movies:

• 2001 Space Odyssey
• AI The Movie
• I, Robot

   Collective Learning Systems & Connecting-the-Dots

   Based on the Collective Learning Theory [Bock 1993]

Collective Learning Systems [Fall 2004] - Professor Mark Happel

   Collective Learning Automata usually use a Static/Uniform compensation policy to update their STM. Therefore the amount of knowledge the CLA has gathered in the STM is generally ignored. Additionally, even if the responses chosen by a CLA are not equally responsible for the final outcome, a Uniform update policy equally rewards/penalizes all of the weights.
In this paper the performance of a CLA with a Progressive/Discriminant compensation policy has been compared to a Static/Uniform policy.
A CLA was implemented to learn how to play a game called “Connecting-the-Dots”.
The performance of the CLA with each form of compensation/update policy was calculated and analyzed.
The conclusions show that a Progressive compensation policy or a Discriminant update policy can dramatically improve the learning rate of a collective learning automaton.

                                        Final Presentation (ppt)