[Computer-go] 7.0 Komi and weird deep search result

Brian Sheppard sheppardco at aol.com
Tue Apr 5 16:00:55 PDT 2011


I spend a lot of time on computer Go, but probably not in the right places.

My work currently focuses on expressing positional features using 3x3 neighborhoods, so that domain knowledge is easier to express as data rather than if/else code. This is useful stuff, to be sure.

Instead, I really ought to build two systems: an MM-based engine to prioritize moves in the tree search, and an SB-based engine for random search in the playout. I am sure that these would have a much greater effect on strength.

But I started the other effort, and I should continue for a while before changing. Probably I will do MM next.

Brian

-----Original Message-----
From: computer-go-bounces at dvandva.org [mailto:computer-go-bounces at dvandva.org] On Behalf Of Aja
Sent: Tuesday, April 05, 2011 1:35 PM
To: computer-go at dvandva.org
Subject: Re: [Computer-go] 7.0 Komi and weird deep search result

I might not really catch what you meant, but I wonder why. :)

Aja

-----原始郵件----- 
From: Brian Sheppard
Sent: Wednesday, April 06, 2011 12:29 AM
To: computer-go at dvandva.org
Subject: Re: [Computer-go] 7.0 Komi and weird deep search result

I don't know if the worst could be worse; UCT convergence for a 1-ply search
is a probabilistic function with an exponential bound. The bound for an
N-ply search is a tower of N exponentials: Exp(Exp(Exp(...Exp()))). Ugh.

Because of this bound, guessing good moves quickly is absolutely vital for
strong play from UCT. Which calls into question why I haven't taken MM and
Sim Balancing more seriously. :-)

-----Original Message-----
From: computer-go-bounces at dvandva.org
[mailto:computer-go-bounces at dvandva.org] On Behalf Of Petr Baudis
Sent: Monday, April 04, 2011 10:27 PM
To: computer-go at dvandva.org
Subject: Re: [Computer-go] 7.0 Komi and weird deep search result

On Mon, Apr 04, 2011 at 12:56:54PM -0400, Brian Sheppard wrote:
> >> MCTS using RAVE prioritization *does* converge to game theoretic values
> in a
> >> binary-valued space.
>
> >Can you reference some more detailed analysis claiming this?
>
>
>
> Theorem: In a binary-valued game of finite length, the RAVE score of all
> winning moves converges to 1, provided that 0 < FPU < 1.

Oh of course, it is obvious. Sorry for being slow and confused.

But it seems it should be possible to prove that even theoretical
convergence in case of RAVE discrepecancies is much slower than with
plain UCT... Might be a fun exercise.

Petr "Pasky" Baudis
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