Yes, I would like to see the references. I also wonder how similar
the statistical estimators are to each other. Do they represent
data and estimates in the same way?
We are trying to move towards a design that mixes intrinsically
different kinds of engines. Neural Networks, UCT/MC, Gnu Go,
tile/pattern matching systems, etc., can all have very different
ways of representing why a particular choice is the highest
valued and what the distance is between the lower valued choices.
Cheers,
David
On 1, Feb 2008, at 12:29 PM, Alain Baeckeroot wrote:
Le vendredi 1 février 2008, David Doshay a écrit :
This is the direction in which we are moving with SlugGo. We also
expect it to be difficult to integrate different approaches, but this
has always been our research direction: when there are multiple
codes which will each give an evaluation of a situation, how does
one design an arbitrator that makes the final decision?
There is a phD student in my lab working on such a topic in speech
recognition (use several different statistical estimators and combine
the informations to get the "best one" or the "best tree").
This give some nice improvements:
more or less 5 engines with 25 % error, give a system which does
20% error,
and this is a huge improvement.
I'll post some references, i guess the tools and methods are more
or less
well known.
Alain
_______________________________________________
computer-go mailing list
computer-go@computer-go.org
http://www.computer-go.org/mailman/listinfo/computer-go/
_______________________________________________
computer-go mailing list
computer-go@computer-go.org
http://www.computer-go.org/mailman/listinfo/computer-go/
