[computer-go] MC Go Effectiveness

[Jacques Basaldúa]

Matt Gokey wrote:

> But what are some of the reasons MC is not even better?
> 1-Since MC engines don't deal with tactics directly, they're not likely
> going to play tactical sequences well for low liberty strings, securing
> eye space, cutting and connecting, ko fights, or ladders, etc.
> 2-Also because most of the play-outs are usually nonsense, they may
> have trouble dealing with meaningful nuances because the positions that
> will lead to these distinctions just don't arise with enough statistical
> frequency in the play-outs to affect the result.  Yet when very
> selective moves are used in the play-outs, too many possibilities can be
> missed.
> 3-Finally, with 19x19 anyway, the size of the board and game tree
> probably limits the practical effectiveness of the sampling and move
> ordering. I don't try to address this last point any further in this
> message.

Very good analysis and I would like to contribute a 4th reason:

As Luke Gustafson pointed out, we have to contemplate the simulation
as a _stochastic process_. We want to determine the conditional 
probability of a win given a particular move is made. And that depends
on the _length of the simulation_. Dramatically! This is a reason 
against scalability of global search MC/UCT. If the simulation is
500 moves long (Chinese rules with recaptures, etc.) the observed
variance at an early move blurs out everything.

Just a simple stochastic process: Count a dollar each time you
correctly predict a p=1/2 coin thrown n=500 times. The expected
average is (obviously) 250, but the expected variance of that 
measure is n·p·(1-p) = 125 proportional to n.

Jacques.