[computer-go] How did MoGo do it?

Mon Mar 5 06:58:34 PST 2007

Also sometimes it scales but not lineal, thats an
issue to. A big one.

Like raw MC, you can get a very good move with less
than 70k simulations, to get a really good move
perhaps you need 400 billons.
I remember there is a paper about this with a 'oracle'
MC engine and they used it to compare to different mc
implementations.

MC oracle was a MC engine running like 100 billon
simulations for every move.

--- Don Dailey <drd at mit.edu> escribió:

> Sylvain,
> 
> What you say is no surpise to me about the
> constraints.  I knew that if
> you improved things, you would eventually have to
> improve or reduce 
> the constraints.
> 
> The simple question I always ask about a feature is
> whether the idea
> is infinitely scalable.   Some idea are very good in
> the short term,
> but in the long term form a barrier to improvement. 
>  Of course that
> doesn't mean the idea shouldn't be used because it
> might be helping
> you.   But at some point you know your program will
> out-grow the
> idea and then it will be holding you back.
> 
> A good example of this is piece square tables in
> computer chess.  It
> was a wonderful way to add a lot of knowledge to a
> chess program
> without slowing it down any.   It's based on the
> idea of looking
> at the starting position,  making a lot of stategic
> decisions before
> you even begin searching (by filling the table with
> values) and then
> doing the search.   This idea has serious limitation
> when you search
> incredibly deep,  but really worked well in the days
> when programs
> could not search  deeply.     I think some programs
> still use
> this today but to a lesser extent - they have
> adjusted accordingly 
> with much more dynamic evaluation. 
> 
> I have this theory that if something isn't "properly
> scalable", then
> you are probably doing it wrong - at least in an
> idealistic sense.
> >From a practical standpoint I have no trouble doing
> these non-scalable
> things if there is no obvious scalable solution that
> can give me the
> same
> short-term improvement.
> 
> AnchorMan is a clear example of this non-scalable
> approach.  A hackery
> of non-scalable patches to try to stop it from
> playing the ugliest of
> moves!   
> 
> - Don
> 
> 
> 
> 
> 
> 
> On Mon, 2007-03-05 at 10:52 +0100, Sylvain Gelly
> wrote:
> > Hello Peter, Hello Don, Hello all,
> > 
> > It is true that I have been mainly working to
> improve the level of
> > MoGo in 19x19. It turned out in my experiments,
> that improving its
> > level in 9x9 with very little simulations was
> significant for the
> > level in 19x19, so it is one reason why the
> limited version of MoGo
> > are running on cgos. So even if the goal was 19x19
> improvements, the
> > improvements appeared in all boardsizes. 
> > 
> > Our first approach to 19x19 was, as you say Don,
> to constrain the
> > board (as explained in our paper), and it brought
> improvements.
> > However, now it is quite the contrary what is
> happening, each
> > improvement allows to remove constrains on 19x19,
> and making it play
> > as if it was a 9x9 board (I mean in the
> parameters). As you may have
> > noticed for example, MoGo is not playing locally
> anymore on 19x19
> > (which gives it an ugly style :-)). 
> > The number of simulations done in this tournament
> was from 10k to 50k
> > per move, depending on the context and the move
> number (simple time
> > management stopping early the thinking if one move
> is clearly better
> > than the others). 
> > For all the details on the improvements, we
> submitted a paper, and I
> > am writing them in my thesis, so you will all
> know, just wait for them
> > to be written :-/.
> > I still strongly believe in the future of MC even
> in 19x19. 
> > 
> > Bye,
> > Sylvain
> > 
> > 
> > 2007/3/5, Don Dailey <drd at mit.edu>:
> > > I'm pretty sure I read that the MoGo team is
> shifting their efforts
> > > towards 19x19 GO.   There are lot's of
> possibilites for research, 
> > > but Mogo already does things to constrain the
> board on 19x19, they
> > > are probably just refining this stuff.
> > > 
> > > - Don
> > > 
> > > 
> > > On Sun, 2007-03-04 at 19:58 -0800, Peter Drake
> wrote: 
> > > > Congratulations to MoGo on winning the KGS
> tournament held earlier
> > > > today:
> > > >
> > > >
> http://www.gokgs.com/tournEntrants.jsp?sort=s&id=270
> > > >
> > > > Even under borderline "blitz" conditions (18
> minutes sudden death
> > for
> > > > 19x19), MoGo managed to beat conventional
> programs like GNU Go.
> > > > (ManyFaces apparently had some
> connection/restarting glitch, so
> > its 
> > > > performance may not be representative.) Of
> course, MoGo also beat
> > all
> > > > the other MC/UCT programs.
> > > >
> > > > How did MoGo do it? I have three hypotheses:
> > > >
> > > > 1) MoGo is completing more runs per second.
> How many is it doing
> > on 
> > > > the machine used in the tournament.
> > > > 2) MoGo is somehow getting more out of the
> runs it does, using
> > things
> > > > like the all-as-first heuristic.
> > > > 3) Each of MoGo's runs is "smarter", through
> the use of
> > heuristics 
> > > > that bias the random games.
> > > >
> > > > My money is on #3. In the limit, of course, a
> very smart program
> > > > could predict the outcome with one MC run for
> each move under
> > > > consideration. 
> > > >
> > > > Would the MoGo authors (and anyone else) care
> to weigh in?
> > > >
> > > > Peter Drake
> > > > http://www.lclark.edu/~drake/
> > > > 
> > > >
> > > >
> > > >
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> > > > computer-go at computer-go.org
> > > >
>
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> > > 
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> > >
>
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> 
> > > 
> 
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