[computer-go] Monte-Carlo Go Misnomer?

[Matt Gokey]

dhillismail at netscape.net wrote:
> -----Original Message----- From: ddoshay at mac.com
> <mailto:ddoshay at mac.com> ...
>>> 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?