[computer-go] Anchor player

[Łukasz Lew]

On 9/26/06, Don Dailey <drd at mit.edu> wrote:
> Lukasz,
>
> Can you test this at 5K and 10K simulations?    Also, have you tested it
> at other depths?

Only D2. This is because I use D2 all as first i.e. I maintain stats n^2 stats,
where n is the number of legal moves.

>
> If it gets over 1500.0  on CGOS with 5K simulations it would be an
> excellent candidate.  It's still interesting if it requires 10K.

I believe this will be not enough, as I use truely random playouts -
I don't use capture heuristic, nor any move ordering.

>
> I am surprised because I had a player that I thought did the same thing
> and it did not test particularly well.  I wonder now if I had an
> implementation bug or it breaks down if you search too deep because I
> think I did this with 4 ply searches.

It's difficult to obtain enoug samples with a constraint that
particular 4 moves
should be played in game.

>
> How deep did you gather the stat's, until the end of the game or did you
> have a stopping rule or limit for the stat gathering part?

In ZG1-d2 I update 30 * 30 stats - for each pair of moves played in
first 30 moves of playout.
BTW Updating in D2 takes 3/4 of running time.

Does Anchor plays only 10k (5k?) games? What kind of enhancements does it have?

Lukasz

>
> - Don
>
>
> > Yes, You're right. 100K simulations, depth 2.
> > To be specific:
> > I make 100K totally random from beginning to end simulations from
> > current board.
> > For each vertexes v1, v2 , colors c1, c2 I gather number of won games
> > where c1 was played at v1 and c2 at v2. (without restriction that v1
> > is played before v2).
> > Then I do fixed depth-2 search evaluating leaves by gathered
> > statistics. (I check for superko here)
>
>
>
>
>
> On Tue, 2006-09-26 at 22:23 +0200, Łukasz Lew wrote:
> > Depth 2, described earlier seems to be a good candidate.
> >
> > Lukasz Lew
> >
> > On 9/26/06, Don Dailey <drd at mit.edu> wrote:
> > > On Tue, 2006-09-26 at 14:03 -0400, House, Jason J. wrote:
> > > > How well does a monte carlo bot with a fixed number of simulations fit
> > > > your profile?  It fits everything except maybe requirement #6
> > > > (resources).  It's also has the added benefit of being extremely
> simple
> > > > to code and for others to understand.
> > >
> > > It fits very closely.  If it's limited to about 10,000 simulations, it
> > > doesn't take much resources but it's a bit on the weak side.
> > >
> > > There are a few enhancements that can be done to salvage the strength
> > > but they must be simple and well defined (and documented) if we want
> > > something that fits all the criteria I mentioned.
> > >
> > > I think it's ideal if the algorithm is documented to the extent that
> > > equivalent clones can be implemented and especially if the description
> > > can remain quite simple.    I wouldn't want this to become a complex
> > > engineering project.
> > >
> > > - Don
> > >
> > >
> > > _______________________________________________
> > > computer-go mailing list
> > > computer-go at computer-go.org
> > > http://www.computer-go.org/mailman/listinfo/computer-go/
> > >
>
>