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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.
Here are two videos. First, our research platform, watercraft, a starcraft clone and second an undergraduate side-project using a kinect to control an ar-drone.
We will now learn much more about artificial intelligence from developing computer-game players than we did from developing chess players. If you are a graduate student or want to be one, I am looking for graduate students interested in evolving Game AI for RTS games. Send email.
I got my Ph.d. from the Department of Computer Science at Indiana University, Bloomington in 1993. My thesis advisor was Greg Rawlins. Others on my committee were Doug Hofstadter , Chris Haynes and Esther Thelene.