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Re: [pygame] PyGameDB coming along well



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 <hugo.yoshi@xxxxxxxxx> wrote:
>   
>> On Tue, Aug 19, 2008 at 10:39 AM, kschnee <kschnee@xxxxxxxxxx> wrote:
>>     
>>> On Tue, 19 Aug 2008 07:49:16 -0700, James Paige <Bob@xxxxxxxxxxxxxxxxxxx>
>>> 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?
>>
>>     
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