Reason for the success of this method is propably something not all-that-interesting. The random sampling and evaluation method works because it allows using simple brute force and random chance against a problem that computer can’t (yet) handle otherwise. I am dan-player myself, and I have watched games where supercomputers with monte carlo -method and go professionals have dueled, and as far as I can tell, the go program makes repeated weird moves that seem to be doing something(hyperactive agent detector), but after a while, original idea is lost and the move becomes bad. I’d estimate that this defect could easily be used against the bot, but even with just good, honest moves, professionals did good against the bot.
My opinion, that I think is widely accepted(not sure though, haven’t been following this much lately), is that Monte Carlo method won’t produce the programs that beat humans. Reason it’s doing so well is not because the method is awesome, but because good go-players use so much of the potential of human brain, encorporating pattern-detection, strategy-planning, many different level of abstractions etcetc(all this effectively. That’s important point, I think), that our current know-how simply can’t match that at all. Which is why bots that try to simulate humans end up losing to moderately experienced(1 year) players even with multiple handicap stones.
Gonna edit/remake that post to make it clearer, but the point anyway was: go is difficult, current go programs do very poorly against even moderately skilled players, so even random sampling beats those attempts. But random sampling is not going to take go programs past human players, and I believe this hype settles as soon as some better decision-making algorhitms are invented.
Reason for the success of this method is propably something not all-that-interesting. The random sampling and evaluation method works because it allows using simple brute force and random chance against a problem that computer can’t (yet) handle otherwise. I am dan-player myself, and I have watched games where supercomputers with monte carlo -method and go professionals have dueled, and as far as I can tell, the go program makes repeated weird moves that seem to be doing something(hyperactive agent detector), but after a while, original idea is lost and the move becomes bad. I’d estimate that this defect could easily be used against the bot, but even with just good, honest moves, professionals did good against the bot.
My opinion, that I think is widely accepted(not sure though, haven’t been following this much lately), is that Monte Carlo method won’t produce the programs that beat humans. Reason it’s doing so well is not because the method is awesome, but because good go-players use so much of the potential of human brain, encorporating pattern-detection, strategy-planning, many different level of abstractions etcetc(all this effectively. That’s important point, I think), that our current know-how simply can’t match that at all. Which is why bots that try to simulate humans end up losing to moderately experienced(1 year) players even with multiple handicap stones.
Gonna edit/remake that post to make it clearer, but the point anyway was: go is difficult, current go programs do very poorly against even moderately skilled players, so even random sampling beats those attempts. But random sampling is not going to take go programs past human players, and I believe this hype settles as soon as some better decision-making algorhitms are invented.