I like sound of SnakeOil, anyone else wanna vote on that too? :) On Tue, Aug 19, 2008 at 6:36 PM, Hugo Arts <[EMAIL PROTECTED]> wrote: > On Tue, Aug 19, 2008 at 10:39 AM, kschnee <[EMAIL PROTECTED]> wrote: >> On Tue, 19 Aug 2008 07:49:16 -0700, James Paige <[EMAIL PROTECTED]> >> wrote: >>> That reminds me of something I read recently. Apparently the first ever >>> computer program that can play Go with near-human skill was developed >>> this year: >>> http://www.usgo.org/index.php?%23_id=4602 >>> >>> Of course, it only beat the human in 1 out of 3 matches :) >> >> >> From what I understand, the reason that AI for these games is so hard is >> that the general method being used is a brute-force lookup table. A chess >> computer has a list of each possible board position that could result from >> its next move, and the possible moves from there, eventually dead-ending at >> some kind of evaluation of whether a board position is "good." So the AI >> picks the move that its lookup table says is most likely to give a good >> result. Because there are 20 possible moves for White at the beginning and >> 400 possible board configurations after Black goes once, that's a >> ridiculously huge database for looking even a few moves ahead. Go has even >> more possible moves, making it harder to take on _with that strategy_. In >> other words the trouble in building a Go computer isn't so much because Go >> is innately more complicated than Chess, but because of the specific way >> that it's complicated. >> >> What's been proposed as an alternative to this strategy is to try to get a >> machine to recognize patterns of pieces, the way that a human might >> recognize a "knight fork" situation, so that it can develop general >> strategies instead of relying on exact known board layouts. If anyone can >> build a program like that, it'd be useful for other purposes too. >> >> > > The method used in beating the Go professional player was a Monte > Carlo algorithm, i.e. the computer did not scan the entire set of > possible moves, but just a random sample from it, and judged these for > their outcomes. It should also be noted that the computer had a > 9-stone handicap (i.e. it could place 9 stones on the board before the > game began), and the computer in question was an 800-core monster > supercomputer. In go, a 9-stone handicap is a huge advantage in a > game. Kind of like taking some one's queen and knights away in a chess > game before the game starts, possibly even worse. The computer did > play at human levels, but certainly not at professional levels, and > the amount of resources required to do even that are tremendous. > > Developing an algorithm smarter and more efficient than brute force is > quite a difficult problem, perhaps even AI-complete. This goes for > chess and other games as well of course, but these have a more limited > possible move set, and so are more amenable to brute-force solutions. > The average amount of possible moves in a chess situation is around > 40, but for go it is closer to 250. > Even so, I think it is easier to increase computational power than it > is to solve the AI problem. With Moore's law still standing, and > brute-force Go algorithms being embarrasingly parallel (each possible > move can be considered separately), stronger Go playing computers will > most likely come from the direction of supercomputers and many-core > machines with the same Monte Carlo approach. > > Back on topic: smoke, mist, or other steam derivatives are a bit lame, > though I am all for a pun on steam, these are a bit obvious. I think > it would also be a good idea(tm) to get either a snake or flying > circus reference into the name. Anyone know any skits that are > smoke/steam/hydrogen related? >
-- Thanks, Richie Ward
