3 ms·
I think the above is trying to say that the percentage of total possible trees that a human would bother considering is much lower in Go than most games, like c
by asolove 12y ago
I think the above is trying to say that the percentage of total possible trees that a human would bother considering is much lower in Go than most games, like chess. So the computational resources needed to do Monte Carlo and get results competitive with human players are much greater.
But the poster is wrong in saying that this means computers need to use heuristics. Indeed, the recent breakthroughs in Go-playing programs come from removing heuristics that previously helped the programs prune the tree space and instead just using Monte Carlo to simulate all possibilities.
- hyperpape 12y agoActually, computers use relatively knowledge heavy playouts these days. http://lifein19x19.com/forum/viewtopic.php?f=18&t=9371 http://lifein19x19.com/forum/viewtopic.php?f=18&t=9371. There's a tricky balance, because incorporating too much knowledge/heuristic use can make the program miss good but surprising moves. Nonetheless, the trend seems to be heavy playouts.
- sehugg 12y agoYes, the strategy for rollouts (the rest of the game after you leave the currently explored tree) are still an area of research. The successful programs usually involve some sort of local shape matching or other heuristic, sometimes trained using ML techniques. One of the surprising results is that having stronger play between opponents in the rollout phase does not always equate with a successful search. Having a balanced strategy -- that is, one that does not introduce a bias for either player -- appears more important.