Kramnik Ties Fritz; Machines Not Yet Our Masters
Maltov writes "World Chess Champion V. Kramnik ties his match against the software Fritz. Details here.
You can also check out a picture gallery and a short history of computer chess."
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If two chess players play perfectly, then the game will always result in a tie. That's one of the big problems with chess as a man-vs-machine benchmark... If both become too good, they will tie all the time.. We might have to move to another game that might be much harder from a computational point of view. (I've been told that the Japanese (or is it Chinese) game of Go is one such game)...
Is it just me, or did someone forget the current score: Machines (1-0-1), Humans (0-1-1).
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If White can force a win, then, in a match of 8 games, each side will have four wins. If Black can force a draw, then in a match of 8 games there will be eight draws.
But as other people have said, determining whether a draw or win can be forced is computationally infeasible. So the game will be interesting for a while.
One oft-quoted complaint by Kasaarov, of the last man-vs-machine match against Deep Blue, was that Deep Blue was programmed with the moves of all of Kasparov's past championship games so it could ostensibly analyze the strategies used by Kasparov beforehand, while Kasparov was not allowed to look at Deep Blue's previous games.
Anyone know if this was ever an issue in this current tournament?
There's 10 types of people in this world, those who understand binary and those who don't.
Anyone know? Not trying to start a flame war here, rather, just curious.
I know that Fritz is supposed to be much more intelligent in its search-tree pruning than Deep Blue was, and not require so much computational power.
There's 10 types of people in this world, those who understand binary and those who don't.
One kind of chess that has been experimented with a bit is where humans play each other, but each has the aid of a computer during the game. Shirov and Anand played a short match like this last year (or the year before), and it seems like an interesting concept. You have the normal human strenghts in judgement, strategy, and intuition coupled with a tool that can process millions of tactical possibilities.
The average slashdotter seems pretty certain of the day when programs, these unbeatable machines, will be able to simply trounce the best humans in one on one competition. But what about a future match with the best chess computer against a top notch grandmaster with his own pc, even a weaker program? Do you people honestly think that human knowledge will simply be obviated by brute force processing power?
The cake is a pie
Hello, sorry, but... Go has not been analyzed and picked apart enough for us to say that it us much more difficult than chess.
Perhaps with the belief among computer chess researchers that chess has been solved will Go soon undergo the same nitpicking that chess has
This game is much more popular than chess in China, Japan, and Korea. Somehow, you seem to assume that these regions are all completely deviod of any programming, AI, or mathematical talent.
These people are obviously just sitting around waiting for us Westerners to solve chess so we can move onto their little problem.
As for your 'points'... they cry of a lack of deep understanding of both Go, and AI
1. Go pieces can be removed from the board, by capturing. Thus opening up more combinations
2. Even if it weren't possible, and a stone was plunked down each time, you'd still have (19x19)! possible moves (a lot, as stated earlier)
3. When chess pieces are removed from the board, it collapses the search tree. On a Go board, it expands it.
4. There are 4 'cells' Remember, in a Ko battle, a space can be empty, but unplayable.
5. The whole cells argument is pretty nonsensical anyway? You are basically discussing bit-depth... in which case, would a black and white face be easier for a computer to recognize than a grayscale, how about color?
6. Facial recognition really has nothing to do with Go in a practical sense. Facial recognition is categorization based on large differences. In go, you have to select the best move based on extremely small differences in extremely similiar layouts.
7. As far as the "million game database" This just will not work, as playing against a human, they'll just do a profitable, but nonsensical move. It is the same thing that happens when studying Joseki. People will know the Joseki, but without an understanding of the principles behind it, it will be useless to them as they will not be able to respond to non-standard moves (GNU Go has a Joseki database I believe).
---Lane
Go and chess are both computations: In both games there are no unknowns but the strategy of the other player. You may not know that the other guy is going to castle, but you know that he CAN castle. Therefor, you can theoretically work out the optimal series of moves from any given state.
Games like backgammon and poker have unknowns - you may know what is in your hand, but you don't know what is next up, nor do you know what the other player has. As a result, given the state you can see, you CANNOT compute a single optimal set of moves - all you can do is probablistically state "most of the time, this would be the best move".
Add to that bluffing - in poker you can bluff the other guy into losing when he should have won.
Now, consider card games like Magic: The Gathering . Not only do you not know what the other guy's next draw is, nor what he has in his hand, you cannot even for certain limit the set of what he can draw very much - "Does he have a Force Of Nature? He might, or he might not."
In addtion, since each card can change the behavior of the other cards, the combinatorial growth of the game state is extremely large. You might be winning, then the other guy plays a card that completely changes how your cards act.
Given the above, much of the game is decided before you even sit at the table - how you construct your deck may decide the game, even before you see your opponent. AND you might change your deck, based on what you observe of the opponent's strategy.
Given the above, what I would like to see would be a computer program that could, given a set of N cards, compose a deck of M card (where M < N), play that deck against an opponent, then compose a new deck from the same N cards that answers the strategy of the opposing player.
When we can do that, THEN I'll believe we have real A.I.
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Anyway, the other posts concerning the search branching factor difference in the two games are right on.
Typically, there are a few hundred possible legal moves in any Go position. It is simple to write an alpha-beta search that does well in chess because of the relatively small branching factor (the free Java AI web book on my site has an example).
Really, Go is an ideal testbed for AI, but currently the best Go programs are good engineering projects, but not really good AI projects. I would consider a great Go project to include these features:
-Mark