Shannon's entropy can also be called information. That translates your argument 
to "you collect more information when the win rate is closer to 50%".

Such arguments can be used in support of both dynamic Komi and simulation 
balancing. I'm not sure how far the argument can be taken, but that's what 
experimentation is for :)

Sent from my iPhone

On Feb 4, 2011, at 8:02 AM, Nick Wedd <n...@maproom.co.uk> wrote:

> In a Monte-Carlo program, the amount of information derived from one playout 
> is given by its enthalpy
>  -p(win).log_2(p(win)) - p(lose).log_2(p(lose)).
> This has a maximum at p(win) = 0.5, and is 0 if p(win) is 0 or 1.
> 
> So, suppose your MC Go-playing program is doing its playouts, and has found 
> several moves which have all won more than 90% of the time.  It can do more 
> playouts with these moves, but this is a poor way of getting more information 
> about which of them is best.  If instead, it pretends that it will have to 
> give an extra 10 points of komi, maybe it finds that these moves now win, on 
> average, only 60% of the time.  Now the enthalpy of the playouts is greater, 
> so it is gathering information faster.  The information is not as good, it is 
> measuring the wrong thing; but it is gathering it more than twice as fast, 
> which should more than compensate.
> 
> Similarly, suppose its best move has won less than 10% of the playouts. It 
> could resign, but let's say it is giving a handicap to a weaker player.  
> Instead of just doing more playouts, it can pretend that it will be receiving 
> extra komi.  Again, the quality of the information per playout then drops, 
> but the quantity goes up, hopefully by more than enough to compensate.
> 
> This seems like an argument for using dynamic komi, adjusted from time to 
> time during each game move.
> 
> Nick
> -- 
> Nick Wedd    n...@maproom.co.uk
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