David Doshay wrote:
I am a physics guy, and my thesis project was a large MC simulation.
The clusters that run SlugGo are usually busy doing MC simulations when
not playing Go.
In general MC needs to sample according to the proper distribution for
the problem. For some problems in quantum mechanics and most in
statistical mechanics, the distribution cleanly partitions into
percentages of the total, and in those simulations it is easy to do
things like generate random numbers and then see what range the random
number is in. For Go I could easily argue that sampling random points
on the board is clearly the wrong distribution, and those programs
using some kind of pattern knowledge are really doing something much
closer to MC simulations rather than true random playout. So, I do not
think that MC is the misnomer. Thinking that pure random playout is the
same as MC is the mistake.
Got it. I was thinking Monte Carlo (name based on the gambling city)
meant it must be random, but looking into it deeper other statistical
sampling methods designed specifically for the problem to reduce the
number of simulations can and are often used. Looks like there is some
level of confusion about this out on the net too. Wikipedia perhaps
needs updating for one.
Thanks for clarifying that.
Go requires a pretty complex simulation to be run and using more
selective moves for the play-outs, if not done very carefully, could
have an adverse effect by being too restrictive or sampling the wrong
distribution. I'm sure random is not the best distribution also, but at
least it is not biased. Are there any methods from other MC research
areas to help find good sampling distributions and reduce variance or is
Go just wildly different than most domains where MC is used?
Matt
_______________________________________________
computer-go mailing list
[email protected]
http://www.computer-go.org/mailman/listinfo/computer-go/
