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, [EMAIL PROTECTED] 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: [EMAIL PROTECTED]
To: [email protected]
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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