[EMAIL PROTECTED] wrote:
-----Original Message----- From: [EMAIL PROTECTED]
<mailto:[EMAIL PROTECTED]> ...
The earliest MC engines were extremely simple and easily
described.
It seems inevitable that someone new to the field will seize >on >
this description, and then combine it with the success of current >
Monte-Carlo engines, leading to unnecessary confusion.
I am not sure what you mean by that. Do you mean that different
people will use the term MC in
different ways and cause confusion in the minds of third parties?
In other words, some people are
using the simplest pure random playout (no consideration ^of
>> distribution at all) and calling that MC
while others are trying hard to keep the moves searched "Go-like."
and thus get different ^results.
Or do you mean that naive programmers will try pure random playout
and wonder why the MoGo folks are doing so very much better, not
>> realizing the importance of getting a decent distribution
for MC to be effective?
I mean both. But IMHO the most serious distinction is whether the MC
is combined with tree search or not. I'm not embarking on a
nomenclature crusade. Just remarking that when people say Monte-Carlo
does this or that, it's often ambiguous what algorithm they are
actually talking about.
This is the condition that led me to first think of and submit the
original question. Sometimes you can't tell what people are talking
about when they mention MC go.
So does anyone have any thoughts on the follow-up comment and question?
That selective move playouts could have adverse effects, and that random
is probably not very good but not biased. Also, what MC distribution
techniques (from general or specific research areas) might be used to
help create better sampling distributions and reduce variance, improving
overall results?
_______________________________________________
computer-go mailing list
computer-go@computer-go.org
http://www.computer-go.org/mailman/listinfo/computer-go/
