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:
> 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.
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