In case anybody out there is wondering what I'm up to.

I've started an AI club at my university, which is great. I mean, we actually didn't have one! Shouldn't every self-respecting computer science department have some crazy AI people stashed in a corner trying to change the world?

Well, we're not there yet-- our short-term goal is to start a small AI competition in a game called Diplomacy. Turns out there's a pre-existing framework for it:

www.daide.org.uk

Also, I've been looking at something that goes by various names, including "competent optimization". I'd call it "intelligent search". Based on genetic algorithms, the idea is to think while searching; more specifically, based on what's been seen so far, attempt to learn the characteristics of good solutions, thus guiding the search.

http://www.cs.umsl.edu/~pelikan/boa.html

http://metacog.org/doc.html

The idea of intelligent search is something I've thought about before, so I was both pleased and disappointed to see it already being researched. This means I can't say I invented it! To to meaningful research in the field, I've got to up the quality of my ideas :).

Of course, really, I haven't done any "meaningful research" at all yet. So it goes. Part of my problem is that I don't focus on one thing well. Also, I seem to like getting started far more than finishing. Coming up with ideas is more exciting than implementing them.

To implement competent search, or at least to implement the Bayesian Optimization Algorithm (which I guess is the current best), I'll need a Bayes Net framework. There are many to choose from, but here's the one I picked:

http://www.openbayes.org/

Probably the Bayes framework in Matlab is the best (except for speed issues), but Matlab costs money (although this Bayes framework for it is free and open source).

http://www.cs.ubc.ca/~murphyk/Software/BNT/bnt.html

So I've been having a lot of thoughts about possible improvements to these intelligent search methods. Mainly, I'm trying to figure out what the theoretically perfect way of doing it would be-- that is, assuming that the only thing that takes computational resources is the actual testing of a point, and so that we can do any complicated analysis we like to decide what point to pick next.