In message <4d1c3938.1040...@snafu.de>, Robert Jasiek <jas...@snafu.de>
writes
Despite his loss of the bet on the surface, I congratulate Darren for
almost correctly predicting the 19x19 computer strength development! It
has been an extraordinarly impressive improvement during the last 3
years! Before 19x19 was more like 10 kyu - now during parts of a game
ManyFaces can hold 1d to 2d level! With some more programming effort
for holding a program's playing strength at a constant level (maybe
also by filtering computer suggested moves by a human approach bias
filter to discard obviously bad moves like A15 in game 3 and by making
endgame more expert-orientated again), this strength can soon be held
during an entire game.
Nick has said that a 2007 Hungarian RAVE paper was the theoretical
breakthrough. Is this its URL?
http://zaphod.aml.sztaki.hu/papers/ecml06.pdf
The site appears to be down though. Is there an alternative URL?
I don't know about "RAVE" - the paper I referred to is available at
http://www.lri.fr/~sebag/Examens_2008/UCT_ecml06.pdf
Nick
ManyFaces was described as an expert system. How does it work today?
How does it use the modern algorithmic theories?
Congratulations also to all the theorists! Without their great
discoveries, programs would still be weak. Might somebody please give
an overview on the relevant theories and how they work?
One thing keeps bothering me though: What does all the strength
improvement give us humans for better understanding the game strategy?
Almost nothing? The information contained in the current calculation
size is not easily translated to human applicable strategic / tactical
knowledge. Other research, which is closer to the human way of go
understanding, by people like Berlekamp, Spight, Cazenawe or myself is
much more useful for players but its playing strength equivalent -
despite a few 10p knowledge exceptions - is still on the 20k level.
Currently there is an extreme gap between computer go theory making
computers strong, maths theory explaining go theory for human
understanding and traditional professional go theory, which fails to
explain well but allows eager and gifted players to succeed by means of
unlimited investment of time and effort. What is still mostly missing
are ways to link well to each other the three major paths towards great
playing strength.
- How can programs learn well from professional knowledge?
- How can programs use well mathematical descriptions of human-like
strategy?
- How can players learn well from strong programs?
- How can further mathematical descriptions of human-like strategy be
derived from strong computer play or its underlying algorithms?
Oh, and of course congratulations to John!
--
Nick Wedd n...@maproom.co.uk
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