[Computer-go] learning patterns for mc go

Jason House jason.james.house at gmail.com
Wed May 19 20:13:15 PDT 2010


I mostly skimmed it, but here's what I got from it: In a simulation,  
pick moves based off the leaf node's RAVE values,  but discount moves  
whose follow-up moves have already been taken.

The tiling is simply a tracking of how effect a move is when combined  
with a specific follow-up move. Near the start of a simulation, this  
would match RAVE values. Deep in a simulation, it's highly situational  
and based on which follow-up moves remain open.

I hope that helps!

Sent from my iPhone

On May 19, 2010, at 7:50 PM, Darren Cook <darren at dcook.org> wrote:

>>> My problem is that I can't find many papers about learning of MC  
>>> playout
>>> policies, in particular patterns.
>>
>> A just published paper about learning MC policies:
>> http://hal.inria.fr/inria-00456422/fr/
>> It works quite well for Havannah (not tested on hex I think).
>
> I struggled with this paper ("Multiple Overlapping Tiles for  
> Contextual
> Monte Carlo Tree Search"), as it wasn't clear to me what a "tile" was.
> Specifically I couldn't work out if they were 2d patterns of
> black/white/empty, or are they are a sequence of moves (e.g. joseki,
> forcing moves, endgame sente/gote sequences, etc. in go)? Or perhaps
> something else altogether?
>
> While I wear the dunce's cap and stand in the corner, is some kind  
> soul
> able to explain the idea in go terms?
>
> Thanks,
> Darren
>
>
> -- 
> Darren Cook, Software Researcher/Developer
>
> http://dcook.org/gobet/  (Shodan Go Bet - who will win?)
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