I work in Genetic and Evolutionary Computing, AI, and their applications to computer games and simulations for education and training (serious games), interactive evolutionary design, machine learning, engineering design, and non-linear optimization.
My work combines genetic algorithms and case-based memory to learn to improve performance on similar problems.
This is a broadly applicable technique and has been used in combinational logic design, combinatorial optimization (tsp, jssp), and in strike planning. The definitive work on this topic is my article on Learning with Case-Injected Genetic AlgoRithms (CIGAR).
Currently, we are investigating how to affordably model human decision making by injecting cases derived from humans to bias genetic algorithms to produce solutions that are similar to human solutions.
Our application areas map well to video games; specifically, 3D real-time strategy (RTS) games like Starcraft. If you are interested in Game AI for computer games, I organized the 2006 IEEE Symposium on Computational Intelligence in Games in Reno.
For more information, please visit my personal website.