[computer-go] Monte-Carlo Go Misnomer?

[David Doshay]

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.

Cheers,
David



On 1, Feb 2007, at 8:33 PM, dhillismail at netscape.net wrote:

>
>       I think of it as a continuum going from "light" to "heavy."  
> Pure random playouts are the lightest. But then you add some rules  
> about filling in eyes, then maybe discourage self-atari,... and the  
> playouts keep getting heavier. I agree with you that the current  
> crop of MC engines are not nearly as go-knowledge naive as the name  
> implies.
>
> - Dave Hillis
>
> -----Original Message-----
> From: mgokey at charter.net
> To: computer-go at computer-go.org
> Sent: Thu, 1 Feb 2007 11:22 PM
> Subject: [computer-go] Monte-Carlo Go Misnomer?
>
> Is MC Go a misnomer for programs in this genre not using simple random
> playouts and combining with other techniques like pattern matching?
>
> Technically, does the general Monte-Carlo method require random or
> pseudo-random sampling?
>
> If so, should we dub a new name for these non-random deep play-out
> sampling based go programs? Maybe Quasi-MC or Directed
> Sampling...
>
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