On Mon, 21 Jul 2008 21:11:05 +0200 Alexander Wagner <[EMAIL PROTECTED]> wrote:
> Giorgio Bellegotti wrote: > > Hi! > > [...] > > this article couldn't be more interesting for me. > > Fine. :) > > > Above all, I find very intriguing the new Monte Carlo > > search method, even if I have a lot of doubts about it (if > > I have correctly understood it). > > Well, I admit that I did not really understand it nor can > judge its value at all. Just some thoughts how I understood > it. > > > I mean: trusting in statistical results can be very > > dangerous in chess (especially in endgames), very often > > there can be only one winning line, but it is enough to > > win the game, instead the Monte Carlo method could > > consider it as a draw line. I think they try to use some _very_ successful ideas from computer Go. The best (or one of the best) bot at the moment plays Monte-Carlo-Go. I don't like, that it works, but it's astonishingly effective. Search for 'mogobot' to find some information. detlef > > I'm not sure about that. I think the point is, that Monte > Carlo simulation is successfull for _large_ numbers of > samples. One would probably say the number of samples was > large enough if the effect you mention has vanished by > averaging over the number of samples. Plus, in Monte Carlo > you actually have a numerical way to estimate the error in > your calculation. > > The only thing I used Monte Carlo simulation for, is > integration of some real valued functions in multiple > dimensions. (I had to integrate f(a,b,c,d,w,x,y,z) over da > db dc dd dw dx dy dz.) There the efficiency of Monte Carlo > rises with the number of dimensions. You gain nothing in say > up to 2 or maybe 3 dimensions, here the classical Gauss is > much faster and more accurate in the same ammount of CPU > cycles. "Taking advantage" means faster convergence at > the same ammount of CPU time admitting for the same > numerical error. Convergence is defined by reaching a > certian error interval in your calculation. > > The point here is, that the stochastic alorigthm just needs > to calculate less points for this compared to the other ones > if you have a large number of dimension. Ie. you have to > use a much finer grid plus you need this grid along each of > your axis in Gauss to reach the same error interval compared > to the numbers of throws of your virtual dice in Monte Carlo > and as each point in the grid needs the complete evaluation > of the complex function you want to integrate... So if you > need to evaluate your function at 16 points for the Gauss > and at 16 points for MC you gain nothing. But if you > increase the dimension by one your Gauss might need 32 > points while your MC stays at 16 or maybe increases to 24. > This is the sort of gain you get. Ie. you calculate the same > but at less grid points. > > > Maybe, I'm missing something... maybe Monte Carlo search > > is only an addition to the normal evaluation, not a > > replacement. > > If I take the analogon, without knowing how the do it, I'd > think that searching the correct move in chess is a pretty > high dimensional problem. The classical approach of weeding > the search tree starting by a line that might get high > evalutiation for the frist moves but then drops to a loss > might converge slower (ie. needs more grid poitns) than just > using a large ammount of random moves and play out the > position at a fast evaluation level. Plus, maybe you find > large error bars in bad moves (you just get everything from > a loss over a draw to a win) compared to very small ones for > the forcing win (there is only one move or you are lost). > > To me it sounds a bit like the triple brain idea of > Shredder's win-GUI. There you take several engines to > evaluate the postion. For forcing lines they may give the > same evaluation. For unclear position however, they differ. > Then you take these different lines from the different > engines and use them as input for a "master mind" that looks > at exactly these lines and takes the one it evalutates best. > This would be statistics with small numbers. The MC approach > sounds a bit like taking 10000 different engines (simulated > by using random moves) and then ... > > But I'm really not an expert at all, I just think in this > analogy to my integration problem. Maybe you could draw some > conclusion from this. > > -- > > Kind regards, / War is Peace. > | Freedom is Slavery. > Alexander Wagner | Ignorance is Strength. > | > | Theory : G. Orwell, "1984" > / In practice: USA, since 2001 > > ------------------------------------------------------------------------- > This SF.Net email is sponsored by the Moblin Your Move Developer's challenge > Build the coolest Linux based applications with Moblin SDK & win great prizes > Grand prize is a trip for two to an Open Source event anywhere in the world > http://moblin-contest.org/redirect.php?banner_id=100&url=/ > _______________________________________________ > Scid-users mailing list > [email protected] > https://lists.sourceforge.net/lists/listinfo/scid-users -- Wisely, and slow. They stumble that run fast. - Shakespeare ------------------------------------------------------------------------- This SF.Net email is sponsored by the Moblin Your Move Developer's challenge Build the coolest Linux based applications with Moblin SDK & win great prizes Grand prize is a trip for two to an Open Source event anywhere in the world http://moblin-contest.org/redirect.php?banner_id=100&url=/ _______________________________________________ Scid-users mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scid-users
