[computer-go] Monte Carlo combined with minimax search

[alain Baeckeroot]

Le dimanche 23 juillet 2006 11:21, Magnus Persson a écrit :
> Quoting Rémi Coulom <Remi.Coulom at univ-lille3.fr>:
> 
> > Peter Drake wrote:
> >> On Jul 22, 2006, at 11:19 PM, Rémi Coulom wrote:
> 
> >>> I am not sure that a continuum would be better than using the 
> >>> probability of winning all the time. At least, I am certain that 
> >>> using the probability of winning all the time is much better than 
> >>> using expected territory all the time.
> >>
> >> Can you say more on this?
> >
> > When I switched from using territory to probability of winning, Crazy 
> > Stone changed from scoring 36% against GNU Go 3.6 at level 10 to 
> > scoring more than 60%, at 16 minutes per game, single CPU.
> 
> I found the same effect for Viking4. I do not have any numbers, but I 
> have never
> seen such a huge improvement of a program (and that with only two lines of
> code).
> 
> Here is my explanation. If expected territory is used the program will 
> be greedy
> in won positions, that is, playing moves that are risky. It will for example
> prefer moves that perhaps will kill a big opponent group 2 times out of 3 for
> an expected win of +10, but will lose the game otherwise. Compared to 
> playing a
> safe move that wins for certain with about +5 points. 
> -- 
> Magnus Persson, 2 Dan
> Zapp at KGS
> Author of the go program Viking
> 

Very clear explanation :)
Gnugo suffers from this, and manage to lose games because of greedyness, 
in positions where a normal wise 15k player would win by more than 20.

I noticed this even if i m only kyu player, and think it is one of the
main "defect" in the engine. Greedyness is a wrong strategy, the aim
is to win, not to win big.
Next time i see such a game i can send it as an example if one wants.

Alain