Re: [computer-go] Minimax with random evaluations

[Antti Huima <antti.huima@xxxxxxxxxxxxx>]

Russell Wallace wrote:

> On Fri, 21 Jan 2005 09:19:50 -0500, Don Dailey <drd@xxxxxxxxxxxxxxxxx> wrote:
>  
> > Evaluation function is:   #define EVAL (8192 - (rand() & 0x3fff))
> > I'm not sure it matters and could just as easily have used rand()
> >    
> I don't understand this - what benefit would minimax search give if
> the evaluation function is random? How is the result different from
> just making a random move?
>
> (Maybe I'm missing something obvious, in which case enlightenment
> would be appreciated :))
>
> - Russell
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>  
If you search one ply as MAX player, you get higher evaluation score 
when the number of available move options increases, because the 
expected maximum value gets higher.

If you search two plys, beginning at MIN player, the lowest score will 
be on move for which the opponent has the least number of answers 
available. This pattern continues inductively.

But the main point is that at the last branch before the search horizon, 
random evaluation scores function as a noisy way to calculate the number 
of options available. In many games having lots of move options 
correlates somewhat with a good position. But, as noted previously, this 
is not very relevant in the context of go.

Regards,

--
Antti Huima (Mr.)
Director, Conformiq Tools
mobile: +358 40 528 8667
email: antti.huima@xxxxxxxxxxxxxxxxx

Conformiq Software Ltd.
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