My dissertation advisor is Risto Miikkulainen of the
Neural Networks Research Group.
I'm interested in automatic discovery of complex multi-modal behavior, particularly in the domain of video games.
Agents that can behave in different manners in response to different situations are crucial for games because
they are so complex, and human players adapt so quickly. I'm particularly interested in the use of multiobjective
evolution and neuroevolution in these domains. Furthermore, because I believe in the power of evolution via
natural selection, I think it should be possible to find domain-independent methods to solve these tasks.
Therefore I also study and develop domain-independent shaping methods to help evolution.
The less expert knowledge, the better.
So far this research has primarily used the BREVE simulation environment.
My source code is available here.
This code is offered freely without guarantee, but feel free to ask me for help
if you are interested. If you are unfamiliar with BREVE,
then I recommend you try out some of the BREVE example code first. The control
class for the simulation is Simulation.tz. Though the file
extension is tz, all files are text files. To get a list of all command line parameters, type: breve -ux Simulation.tz io off help
Lately I've moved on to working in the domain of Ms. Pac-Man using the
Java implementation available here.