[Computer-go] semeai example of winning rate

[Don Dailey]

On Thu, Jan 20, 2011 at 8:50 AM, terry mcintyre <terrymcintyre at yahoo.com>wrote:

> *From:* Stefan Kaitschick <Stefan.Kaitschick at Hamburg.de>
> *Sent:* Thu, January 20, 2011 8:28:53 AM
> *
> * on 20.01.2011 05:35, Darren Cook wrote:
> > Told you I was getting passionate about this point;-). Though actually
> > I wasn't even thinking so much about number of playouts, more about
> > other ideas such as first spending a few seconds on static semeai or
> > life-death analysis on the root position then using that information to
> > influence the playouts the same way RAVE values are used.
> >
> > Darren
>
> > Semeais and L&D are concerned with groups, eyes, libs, connections ect.
> MCTS is concerned with winrates for moves.
> > The gap between them is hard to bridge. At a minimum, the "why" will be
> lost in translation.
> > And if the "why" is lost, it becomes impossible to reason if the original
> canditions are still met.
> > This is especially significant because S+L&D information would be
> propagated top-down, not botton-up like RAVE.
> > I think the "next generation" bots will have to manage much more complex
> information.  It will be paradise lost. :-)
>
> > Stefan
>
> Perhaps the top-down analysis, not the bottom-up MCTS, should be in the
> driver's seat, and the MCTS should be used to compare positions which are
> considered "playable" according to the higher-level analysis?
>

It's going to be super difficult no matter how you slice it.    Is there any
way to have a pre-processor that looks at the starting board before the
search and provides some direction that is passed to the tree search and
playouts?

Don





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