[computer-go] Bot ratings and strength

[Don Dailey]

By the way,  in addition to the other stuff I mentioned, when you play a
random game, DO NOT include the pass move until you are completely out
of moves (we don't count eye filling moves as legal moves in this
context.)

I'll help you get this over 1200.

- Don




On Thu, 2006-08-31 at 20:29 -0400, John Doe wrote:
> I understand that some authors may not wish to reveal too many details
> of their programs, but I am confused and a little frustrated by my
> inability to make a bot that climbs even above 800 in rating on CGOS.
> I am following a fairly straightforward Monte Carlo style approach --
> playing out random simulations for each potential move from a position
> and choosing the one that led to the most wins.  The random moves
> within the simulations avoid filling single-point eyes (using the
> counting of corner-touching enemies discussed on thi list) and playing
> into self-atari.  The top-level move chosen to simulate next is based
> on the UCT-style algorithm, although I do not (yet) keep any more of
> the tree in memory.  I have tried to make the simulations as fast as
> possible; the number run is based on remaining time, but is usually
> around 50,000 for a move. 
>  
> My basic question is this: what makes some other similar programs so
> much stronger?  I read the Sensai library descriptions for AnchorMan
> and ControlBoy and see that they are very similar, yet do only 5,000
> simulations, and yet are much much stronger programs overall.  Does
> this difference come primarily from the benefits of keeping more of
> the tree in memory?  From better heuristics for selecting top-level
> and/or simulation moves?  I don't imagine anyone will have a
> completely definitive answer for this, but I am just at a loss at this
> point.  Any guidance would be most appreciated. 
>  
> ~ Jon
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