In message <20091124193826.303...@gmx.net>, Ingo Althöfer
<3-hirn-ver...@gmx.de> writes
Jeff Nowakowski wrote:
I think this game [go with Hahn scoring; IA] is clearly more
difficult than a binary win/loss game.
That is one of the possible question, and I also vote for "yes",
as normal go is simply a Hahn-Go veriant with "coarsened" evaluation.
But the "additive" property of Hahn scoring makes life easy for players.
If the board has become separated into regions that do not interact,
players can just work out what they think is the biggest local move on
each part of the board, and then make the biggest of these moves. This
calculation is correct for Hahn scoring, but not for normal scoring, or
indeed any other way of scoring. (I am not talking about tedomari
effects here, which are rare and small; I am talking about the handling
of uncertainty.)
In fact, I believe that of all the ways of converting from the board
score to the object of the game, Hahn scoring is the uniquely easiest
and least interesting.
Even more interesting might be this question: Assume, you have humans
and bots, all of same strength in traditional go. Which of the groups
will be better in Hahn-Go ?
The humans.
All the currently strongest bots use some form of MC UCT, which works
surprisingly well for traditional Go and is not intended for Hahn Go.
Most humans below around 5-kyu play traditional Go as if they were
playing Hahn Go (because they can't count, or because they have never
learned to apply the results of their counting). So I expect humans at
the same level as leading bots, i.e. around 1-dan, can still manage to
adjust their play for Hahn Go if asked to.
Nick
Dave Hillis proposed:
It's interesting to consider the problem of writing an agent
to make side bets. There could be a pool of spectator bot,
each calculating an estimate of the final score, after every
move, and placing wagers.
I like this idea, even in the generalized version,
where humans and bots are allowed to bet on the outcome(s).
Ingo.
--
Nick Wedd n...@maproom.co.uk
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