[Computer-go] Pebbles opening learning

Petri Pitkanen petri.t.pitkanen at gmail.com
Thu Oct 14 21:49:13 PDT 2010

I presume this is for small boards? Hard to see in 19x19 that doing such
statistics would be meaningful.


2010/10/14 Brian Sheppard <sheppardco at aol.com>

> The Mogo team (among others) published extensive descriptions of how to
> write an opening library that learns. I recommend following their idea,
> rather than mine. :-)
> But if you don't have the time to do things properly, and you don't want to
> lose the exact same game over and over, then you might use Pebbles simple
> adaptive technique.
> Pebbles repeats moves that won the last time they were played.
> Specifically:
>        1) after every game, mark all positions in which the winner moved as
> "winning-last-time".
>        2) In every game look up all successors of the current position.
>        3) Choose randomly among successors that were "winning-last-time".
>        4) If no "winning-last-time" then search.
> This simple system is surprisingly robust. In particular,
>        a) your program learns from opponents that defeat it.
>        b) your program repeats moves that previously won.
>        c) The selected moves have a strength that is "lower-bounded" by
> your program's native search strength, because
>                i) A move either came from your own search, or
>                ii) from a previous game in which an opponent defeated you.
> The weakness is in step 4: search. It is possible to have a winning
> position, but your search can't find the play.
> It is possible to upgrade step 4 so that the process is asymptotically
> optimal. That is, the system converges on maximal play. Pebbles hasn't
> actually implemented that feature, but I will have to implement that
> eventually in order to climb the ladder.
> Brian
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