Olivier Teytaud wrote:
> As programs improve, my humble opinion is that computational power
> becomes less and less efficient as now the main trouble is the
> systematic bias for some positions (semeais or too complicated
> life-and-death situations) - parallelization is not required.

I wondered about this when checking my facts for the Shodan Go Bet page
(http://dcook.org/gobet/), as it seemed CrazyStone, ManyFaces and Zen
are getting similar results on, say, 8-cores as Mogo was getting on big
supercomputers. (With the usual disclaimer that sample size is small so
the error bars are big...)

And David Fotland commented [1] with effectively the same thing: current
MCTS programs won't be European 1-dan no matter how much computer power
they have.  (Instead algorithm issues need to be fixed.)

Don will jump in and say but the algorithms scale, this cannot be true.
But, while that may be the case, perhaps we can say that they are
hitting a wall in their observable playing strength against non-MCTS
players (such as humans) at higher levels. In [2] I touched upon how the
nature of the game changes at higher levels, and how scaling results
obtained between weaker players may not apply at those higher levels. I
was talking about pure random playouts in that article, but the
systematic bias Olivier mentions can lead to the same problems as no
bias at all...

I'd love to hear what David and others think about the kind of
algorithms (or knowledge?) that are needed to get past this wall.

Darren


[1]:
http://answers.polldaddy.com/poll/1614035/?view=results

[2]:
http://dcook.org/compgo/article_the_problem_with_random_playouts.html


-- 
Darren Cook, Software Researcher/Developer
http://dcook.org/gobet/  (Shodan Go Bet - who will win?)
http://dcook.org/mlsn/ (Multilingual open source semantic network)
http://dcook.org/work/ (About me and my work)
http://dcook.org/blogs.html (My blogs and articles)
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