Greetings, everyone.

My research group is exploring coevolution as game tree search,  
specifically for the game of Go. We are aware of many attempt to use  
genetic algorithms to evolve players or board evaluators over the  
course of many games, but we are trying to use coevolution to find  
the best move (or at least a good move) during the course of a single  
game. The individuals being evolved are partial strategies, i.e.,  
subsets of the game tree starting at the root but not extending to  
the leaves. When we have two individuals play against each other as  
part of fitness testing, we use the individuals to determine moves  
until we fall of the bottoms of the trees, then finish the game  
randomly, as a Monte-Carlo playout. We've had some preliminary  
success, and will be presenting a paper at GEM'08 next month.

My question: is anyone aware of other work in this area? We haven't  
been able to find any.

Thanks in advance,

Peter Drake
http://www.lclark.edu/~drake/

I had a look at your paper at http://webdisk.lclark.edu/drake/publications/drake-gem2008-final.pdf
. Interesting. Haven't seen this idea before (but that doesn't mean to
say it doesn't exist). It seems vaguely reminiscent of LCS's, but not
the same, and as you point out, a lot of these things (GA's,
reinforcement learning, LCS...) can be described as sort of random
search with a bias towards things that are learned to be good.
Certainly seems like the idea could be applied to other similar tasks.

cheers, phi

On Jun 23, 12:18 pm, Peter Drake <dr...@lclark.edu> wrote: