[computer-go] Monte-Carlo Go Misnomer?

[Matt Gokey]

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