On Mon, Jun 3, 2013 at 9:14 AM, Stefan Kaitschick <
[email protected]> wrote:

> >
> > My original question was meant for non-resigning bots. The observation:
> > Also a losing bot starts to play lazy towards the end. And the question
> > is:
> >
> > To which results do laziness of the winning bot AND laziness
> > of the losing bot together lead typically?
> >
> > Ingo.
>
>
> A losing bot is never lazy. It can get despondent though. :-)
> The most interesting result is that a slight bonus on higher margins
> can improve the success rate, even though it might slightly reduce the
> bots internal winrate. I would venture the hypothesis, that increasing
> the margin from 0.5 to 1.5 is worth the greatest winrate "investment"
> by far. Messing up the final ko is always a possibility.
>

This basically comes down to opponent modeling.   You can improve the
success of a bot by trying various things to make it play more
aggressively,   but in some theoretical sense you are reducing the strength
of the bot.     In a similar way I can beat weaker players at chess more
easily by playing unsound moves.

It is possibly the case that you could even improve the play in a real
sense if such tricks address an actual weakness in the program,  such as
providing a buffer against poor understanding of certain endgame shapes
(Nakade.)      That is like a "band-aid" fix but not necessarily a bad
thing.     We do whatever works.    There are basically 3 reasons to use
such tricks:

  1.  To improve the style of a program for cosmetic reasons.

  2.  To improve the results against other players (opponent modeling.)

  3.  To get an actual improvement in strength with a general band-aid fix
(as opposed to addressing the actual issue.)

In handicap games one side basically starts from a dead lost position,   so
even the top players rely heavily on opponent modeling.    For a strong bot
you would want the program to "stay in the game" until the opponent makes
enough mistakes to make a win possible.

Don






>
> Stefan
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