Strong engines playing each other draw more often than weak engines playing each other.
Now, if we draw a graph (elo/draw%), find a best fit curve and extrapolate it, we should be able to estimate the strength of engines playing perfect chess (=100% draws).
Does this seem reasonable? Has it been done?
Perfect play
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Re: Perfect play
Yes, I have done some study on CCRL data, by filtering only programs which have an average opposition within 20 elo of their rating.
A better study would be to filter on a game by game basis instead of player by player, but I've had no luck with database programs (CCRL database seems too big for SCID to filter).
Below the results.
A better study would be to filter on a game by game basis instead of player by player, but I've had no luck with database programs (CCRL database seems too big for SCID to filter).
Below the results.
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Re: Perfect play
Very reasonable result. Between 3800 and 5000 CCRL ELO points was my result with diminishing returns (ELO value of doubling time or nodes).
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Re: Perfect play
If engines make 50 ELO in a year then we need to wait ten years before we know 3800 ELO is right estimate for perfect play.
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Re: Perfect play
Why would not it be a S-curve ?
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Re: Perfect play
I don't think extrapolations are logical when it comes to current chess engines. Consider the concepts that engines do not grasp at all, such as "always/never" (different types of fortresses and dominations) or cyclic improvements (see Blathy studies): engines built around the selective brute force approach cannot grasp these, so will never play perfect. On the other hand, if a new type of engine is built (e.g. neural networks), then the graph related to the "old type of engines" is completely irrelevant
Have fun though
Have fun though
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Re: Perfect play
In TCEC superfinal, engines gets 90% of Draw easy even on imbalanced opening. They play with a strenght around 3500, so I don't think the graph here is accurate.
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Re: Perfect play
100% draws does not necessarily mean perfect play. It only means that the difference of the imperfections is not large enough to push closely matched players out of the draw zone.
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Re: Perfect play
There is nothing special about chess "with different types of domination" and other fancy things. The odds are it will be weakly solved a-la checkers using this type of engines as today in some decades to come. With neural networks using alpha/beta search and reductions, Giraffe chess engine enjoys pretty same draw rates and gains with time/depth.Kornrade wrote:I don't think extrapolations are logical when it comes to current chess engines. Consider the concepts that engines do not grasp at all, such as "always/never" (different types of fortresses and dominations) or cyclic improvements (see Blathy studies): engines built around the selective brute force approach cannot grasp these, so will never play perfect. On the other hand, if a new type of engine is built (e.g. neural networks), then the graph related to the "old type of engines" is completely irrelevant
Have fun though
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Re: Perfect play
Use the tool "eloGap" in 40H-PGN tools (see www below for link). It is a command line tool that does game by game filtering based on Elo difference.megamau wrote:Yes, I have done some study on CCRL data, by filtering only programs which have an average opposition within 20 elo of their rating.
A better study would be to filter on a game by game basis instead of player by player, but I've had no luck with database programs (CCRL database seems too big for SCID to filter).
From its readme:
"eloGap" extracts games in which the players' Elo gap (absolute
value of their Elo difference) is less than or equal to a
user-specified maximum Elo gap.
For example:
eloGap alpha.pgn 100
extracts all games in alpha.pgn with an Elo gap from 0 to 100.
The output file is "outF.pgn". Games not extracted to "outF.pgn"
are in "excludeF.pgn".
"eloGap" does not extract games missing an Elo tag.
Syntax: eloGap filename.pgn elo_distance
Example: eloGap alpha.pgn 100
Output: outF.pgn, excludeF.pgn