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Grandmaster-Level Chess Without Search

arxiv.org

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Re: Grandmaster-Level Chess Without Search

#31

Post by londons_explore »

jedberg wrote: post #24
xianshou wrote: post #10

The path to AGI: 0. Have model A. 1. Use Monte Carlo with A to get supervised data. 2. Train model B with data from A. 3. Use Monte Carlo with B to get supervised data. 4. Train model C with data from B...

That's basically how OpenAI is working. They use generated training sets from one model to train the next model (plus other stuff with it). But the "other stuff" is pretty important. That is what pulls it away from just constantly re-amplifying the bias in the initial training data.

I still want to see some examples of a "mistake" in the training data getting detected or reduced.

For example, somewhere in the training data the string "All cats are red" should get detected when lots of other data in the training set contradicts the statement.

And obviously it doesn't have to be simple logical statements, but also bigger questions like "how come the 2nd world war happened despite X person and Y person being on good speaking terms as evidenced by all these letters in the archives?"

When AI can do that, it should be able to turn our body of knowledge into a much bigger/more useful one by raising questions that arise from data we already have, but never noticed.

Re: Grandmaster-Level Chess Without Search

#32

Post by billforsternz »

paulddraper wrote: post #7
zniturah wrote: post #5

They do use Stockfish for playing thought … “To prevent some of these situations, we check whether the predicted scores for all top five moves lie above a win percentage of 99% and double-check this condition with Stockfish, and if so, use Stockfish’s top move (out of these) to have consistency in strategy across time-steps.”

But only to complete a winning position.

The process of converting a completely winning position (typically one with a large material advantage) is a phase change relative to normal play which is the struggle to achieve such a position. In other words you are doing something different at that point. For example, me as weak FIDE CM (Candidate Master) could not compete with a top grandmaster in a game of chess, but I could finish off a trivial win.

Edit: Recently I brought some ancient (1978) chess software back to life https://github.com/billforsternz/retro-sargon. These two phases of chess, basically two different games, were quite noticeable with that program, which is chess software stripped back to the bone. Sargon 1978 could play decently well, but it absolutely did not have the technique to convert winning positions (because this is different challenge to regular chess). For example, it could not in general mate with rook (or even queen) and king against bare king. The technique of squeezing the enemy king into a progressively smaller box was unknown to it.

Re: Grandmaster-Level Chess Without Search

#33

Post by dayjaby »

IIAOPSW wrote: post #26

Slightly off topic but am I the only one that approaches strategy games by making a "zeroth order approximation". Eg find the shortest path to victory under the (obviously faulty) assumption that my opponent does nothing and the board is unchanging except for my moves. Now find my opponents shortest path to victory under the same assumption. Then evaluate, if we both just ignore each other and try to bum rush the vic…

On beginner level this might work, but if people are more competitive they begin to realize the benefit of not only playing the own game, but reading the enemies plan (e.g. scouting in Starcraft/AoE2) to counteract it as much as possible.

Chess against humen is different. Usually, there is no path to victory, only to remis. People just follow strategic plans that people told them would be slightly beneficial later on. Along following that strategic plan people mess up and the first one to realize that the opponent messed up usually wins. Like having a piece advantage of 2-3 is usually considered a win already.

Re: Grandmaster-Level Chess Without Search

#35

Post by Kon5ole »

jedberg wrote: post #20

Now do Go. :)

This used to be a comforting thought whenever computers beat humans in chess, but I think that time has passed. The paper mentions AlphaZero [1], which has beaten AlphaGo, which beat Lee Sedol back in 2016 [2].

[1] https://en.wikipedia.org/wiki/AlphaZero

[2] https://en.wikipedia.org/wiki/AlphaGo_versus_Lee_Sedol

Re: Grandmaster-Level Chess Without Search

#36

Post by littlestymaar »

zniturah wrote: post #22
paulddraper wrote: post #17

Earlier quoted context omitted.

Positions with 99% win percentage are not decisive for GM vs non-GM rating.

Proof? For winning any game at some point (at the end of the game) there will be a position with >99% winning chances. The move that follows are decisive.

That's not how chess works. The move that follow aren't usually decisive unless you don't know how to play the game and make enormous mistakes.

Anyone that knows how to play can beat a GM with a big enough advantage at the end of the game (which is what's reflected in the win probability).

Re: Grandmaster-Level Chess Without Search

#37

Post by joe_the_user »

paulddraper wrote: post #18

There is rampant misunderstanding of some parts of this article; allow me to help :) The "no-search" chess engine uses search (Stockfish) in in two ways: 1. To score positions in the training data. This is only training data, no search is performed when actually playing. 2. To play moves when the position has many options with a 99% win rate. This is to prevent pathological behavior in already won positions, and is n…

Sort of but it seems a bit of a cheat.

Neural networks are universal approximators. Choose a function and get enough data from it and you can approximate it very closely and maybe exactly. If initially creating function F required algorithm Y ("search" or whatever), you can do your approximation to F and then say "Look F without Y" and for all we know, the approximation might be doing things internally that are actually nearly identical to the initial F.

Re: Grandmaster-Level Chess Without Search

#38

Post by janalsncm »

famouswaffles wrote: post #13

Well without explicit search would probably be more accurate. They note that though in the paper: >Since transformers may learn to roll out iterative computation (which arises in search) across layers, deeper networks may hold the potential for deeper unrolls.

We don’t know if it’s using implicit search either. While it would be interesting if the network was doing some internal search, it’s also possible it has just memorized the evaluations from 10M games and is performing some function of the similarity of the input to those previously seen.

Re: Grandmaster-Level Chess Without Search

#39

Post by jackblemming »

joe_the_user wrote: post #37
paulddraper wrote: post #18

There is rampant misunderstanding of some parts of this article; allow me to help :) The "no-search" chess engine uses search (Stockfish) in in two ways: 1. To score positions in the training data. This is only training data, no search is performed when actually playing. 2. To play moves when the position has many options with a 99% win rate. This is to prevent pathological behavior in already won positions, and is n…

Sort of but it seems a bit of a cheat. Neural networks are universal approximators. Choose a function and get enough data from it and you can approximate it very closely and maybe exactly. If initially creating function F required algorithm Y ("search" or whatever), you can do your approximation to F and then say "Look F without Y" and for all we know, the approximation might be doing things internally that are actua…

No, arbitrarily wide neural networks are approximators of Borel Measurable functions. Big difference between that and “any function”. RNNs are Turing Complete though.

Re: Grandmaster-Level Chess Without Search

#40

Post by janalsncm »

joe_the_user wrote: post #37
paulddraper wrote: post #18

There is rampant misunderstanding of some parts of this article; allow me to help :) The "no-search" chess engine uses search (Stockfish) in in two ways: 1. To score positions in the training data. This is only training data, no search is performed when actually playing. 2. To play moves when the position has many options with a 99% win rate. This is to prevent pathological behavior in already won positions, and is n…

Sort of but it seems a bit of a cheat. Neural networks are universal approximators. Choose a function and get enough data from it and you can approximate it very closely and maybe exactly. If initially creating function F required algorithm Y ("search" or whatever), you can do your approximation to F and then say "Look F without Y" and for all we know, the approximation might be doing things internally that are actua…

A sufficiently large nn can learn an arbitrary function, yes. But stockfish is also theoretically perfect given infinite computational resources.

What is interesting is performing well under reasonable computational constraints i.e. doing it faster/with fewer flops than stockfish.

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