[Computer-go] How to Research Brilliantly?

[valkyria at phmp.se]

Quoting David Ongaro <david.ongaro at hamburg.de>:


> Some say the discovery of MCTS had to come this late, because it   
> needs computer power to be efficient. But I think this misses a   
> great deal of the story. I'm sure todays MCTS programs can beat the   
> hell out of expert system Go programs even on year 2000's hardware.   
> Even though the time was ripe in 2006, I think with a more   
> systematic approach in Go we could have get there much earlier.   
> Therefore Rémis achievement cannot be overestimated.

I am running Valkyria3.5.9 on a Pentium4 2.8Ghz single core processor  
which is about 10 slower than a modern i7 running 4 threads.

 From the current Bayes Elo rating

72  	Valkyria3.5.9_P4Bx  	2576  	8  	8  	17664  	77%  	2173

Gnugo as the reference of the state of the art

374  	gnugo-3.8-l10F  	1839  	7  	6  	31622  	42%  	1880

It took me a long time to beat gnugo on 9x9 with the help of  
MC-evaluation. The problem was I never discovered to do MC *Tree  
Search* in the way it is done today. This I have to thank Crazystone  
and Mogo for.

And with a modern standard i7 CPU

9  	Valkyria3.5.17_4cx  	2748  	21  	20  	2696  	85%  	2261

one gets 200 Elo.

The thing was when I started doing MC-evaluation with the "wrong" tree  
search method it was extremely inefficient. I really needed a lot of  
faith in MC to continue working on. The idea was already there. But it  
took a lot of work on the simulations to get to a point where it  
showed how good the idea can be.

The latest version of Valkyria is almost rated 1000 Elo higher than  
gnugo. 200 Elo is hardware development during these years. Reaching  
gnugo strength is due to the search algorithm but the rest is to all  
the tewas patterns and heuristics in the heavy playouts.

Best
Magnus