[Computer-go] News on Tromp-Cook ?

[Nick Wedd]

In message <4D1C3938.1040401 at snafu.de>, Robert Jasiek <jasiek at 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    nick at maproom.co.uk