[Computer-go] learning patterns for mc go

Hendrik Baier hendrik.baier at googlemail.com
Mon Apr 26 14:53:48 PDT 2010


Hello list,

I am a Master's student currently working on his thesis about certain 
aspects of Monte-Carlo Go. I would like to pose a question concerning 
the literature - I hope some of you can help me out!

My problem is that I can't find many papers about learning of MC playout 
policies, in particular patterns. A lot of programs seem to be using 
Mogo's 3x3 patterns, which have been handcoded, or some variation 
thereof. A lot of people have tried some form of pattern learning, but 
mostly to directly predict expert moves it seems, not explicitly 
optimizing the patterns for their function in an MC playout policy. 
Actually, I am only aware of "Computing Elo Ratings of Move Patterns in 
the Game of Go", where patterns have been learned from pro moves, but 
then also successfully used in an MC playout policy; and "Monte Carlo 
Simulation Balancing".

Considering the huge impact local patterns have had on the success of MC 
programs, I would have expected more attention towards automatically 
learning and weighting them specifically for MC playouts. There is no 
reason why patterns which are good for predicting experts should also be 
good for guaranteeing diverse, balanced playout distributions. Have I 
missed something?

Or how did your program come to its patterns? I'd be interested. Did you 
maybe even try learning something else than patterns for your playout 
policy?

cheers,
Hendrik



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