[computer-go] New scalability study : show uncertainty ?

[Jacques Basaldúa]

> I don't think "only uniformly random playouts will scale to 
> perfection" because what we need for playouts is not just a simple 
> average of final scores but a maximum (in negmax sense) score.  It 
> should be the perfect evaluation function.

> In other words, as MC simulation is a way to get an average of a 
> value, when applying it to optimization problems we need some way to 
> focus the simulations to the _peak_ in a state space.

> It may be obvious when one consideres L&D problems where the best move 
> that leads to the maximum score (live) is only one and all other moves 
> are bad.  At such positions it's almost no sense to simulate all legal 
> moves with same probability.  So, IMHO, biasing simulations is not 
> just a speed-up technique but is essentially important.

I agree, but what I meant about uniformly random playouts is the following:
What makes a move outstanding is being unpredictable. For a total novice,
playing at the key point of a bulky five may look like a touch of genius,
but when you learn a little, its an obvious move. The difference between a
5p and a 9p may be one or two moves nobody can predict (except a 9p). When 
we add knowledge we find the _ordinary_ good moves faster, we make weaker 
moves less probable, but that comes at a price, the price of making outstanding 
unpredictable moves less probable also. Perhaps that introduces a ceiling. 
I thought that was what you were also pointing. Of course, I don't claim 
uniformly random playouts are good, I just claim that they should (just as an 
infeasible theoretic argument) scale to perfection, of course that scaling 
doesn't have to be linear.

Jacques.