[Computer-go] learning patterns for mc go

David Doshay ddoshay at mac.com
Tue Apr 27 10:28:07 PDT 2010


Because of my background in physics, where I did my thesis work using a very large MC simulation to study phase transitions, I have the expectation that MC simulations should work much better when properly biased. In physics, we have a theoretic basis for the bias, often from Boltzman or other appropriate statistics. Unfortunately, not so in Go.

Here is a pointer to all of our work on computer go: 

	http://users.soe.ucsc.edu/~charlie/projects/SlugGo/

The papers to look at are the last 3 at that link, all from Jen Flynn. Her project explored that question from a sampling perspective:

	• Here are three unpublished quarterly reports from Jennifer Flynn.
		• SlugGo Summer 07 Report: Predicting the Winner of 9x9 Computer Go Games using Patterns as Evaluators
		• SlugGo Winter 08 Report: Using 5x5 Tiles with Libego
		• SlugGo Spring 08 Report: Using Patterns in Libego Playouts

Unfortunately, we did not get anything we could call a positive result.

Cheers,
David



On 26, Apr 2010, at 2:53 PM, Hendrik Baier wrote:

> 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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