I'm curious what you guys think about the scalability of monte carlo with UCT. Let's say we took a cluster like that which was used for the Mogo vs. Kim game. Then lets say we made 128 of these clusters and connected them together efficiently. Putting aside implementation and latency issues... what kind of stones-strength increase would you imagine?
Its a pretty arbitrary guess.. but do you think one stone improvement... or that this would alone be enough to beat a pro even? I am wondering because there could be a weakness or limit in MC w/ UCT. I am only now learning about the UCT addition... but there are vast numbers of possible games that are never visited during the monte carlo simulations. The random stone simulations are pretty random aren't they? I am reading some of the papers on the UCT addition... and that does seem to show certain branches to be "better" and worth more time. Pro players may have a counter-strategy that might come out as Mogo is tested at higher levels of play. Perhaps there will be a need to combine MCwUCT with heuristics or more knowledge based play. Going the route of heuristics seems unpleasant and the promise of using a more computational method would be great. However... if MC techniques alone have a diminishing return... the route of heuristics might come back (or perhaps a whole new paradigm for game algorithms). I am still secretly on the side of human go beating the machine.. but the recent match really changed my view on topic and really showed the value of statistical analysis. I am just wondering about what kind of roadblocks might show up for the monte carlo techniques.
_______________________________________________ computer-go mailing list computer-go@computer-go.org http://www.computer-go.org/mailman/listinfo/computer-go/
