Hi I would like 'IcedGames' or 'SolidGames' as another state of steam ;-)
(or maye 'IcedMonty' or 'SolidMonty') ~DR0ID Richie Ward schrieb: > 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? >> >> > > > >
