Le vendredi 7 mars 2008, Petr Baudis a écrit :
> This has nothing to do with black/white distinction. The idea is to
> dynamically adjust the komi to make UCT to aim at higher and potentially
> less sure win or lower and potentially more sure loss. Of course,
> depending on whether it takes black o
On Fri, 7 Mar 2008, Petr Baudis wrote:
> On Thu, Mar 06, 2008 at 04:33:16PM -0800, Dave Dyer wrote:
> >
> > To a first order approximation, would changing the komi change the
> > rankings? Presumably, programs are playing the same number of games
> > as black and white, so any "unfair" advanta
On Fri, Mar 07, 2008 at 08:04:37AM -0500, Thomas Wolf wrote:
> I assume that when you change komi dynamically, all that was learned
> by MC so far under the different komi value is useless/wrong.
But what are actually your reuse rates? With the standard UCB1 formula,
I find reusing branches from e
I honestly think there are better ways to handle this, if you must,
other than changing the goal to a losing goal. At least give the
computer the right goal (winning) and adjust from there.
If I were trying to solve this "problem", the solution I would look at
first would be to pre-proces
On Fri, Mar 07, 2008 at 12:43:42PM -0500, Don Dailey wrote:
> I honestly think there are better ways to handle this, if you must,
> other than changing the goal to a losing goal. At least give the
> computer the right goal (winning) and adjust from there.
>
> If I were trying to solve this "
Petr Baudis wrote:
> On Fri, Mar 07, 2008 at 12:43:42PM -0500, Don Dailey wrote:
>
>> I honestly think there are better ways to handle this, if you must,
>> other than changing the goal to a losing goal. At least give the
>> computer the right goal (winning) and adjust from there.
>>
>>
Hi,
I'm wondering about how to best make my Monte Carlo playouts within
UCT heavier and which pieces of domain knowledge to better use to bias
the tree and which ones to apply during the playouts, so I would like to
ask about previous experiences.
Currently, I do three basic hints that I ch
Hi,
does anyone know of any pre-made open framework using genetic
algorithms that one could use to tune various parameters of a bot? I
have about 6 independent parameters for my bot so far that I would like
to find best values for (from domain-specific knowledge hint rates to p
parameter of th
It's almost always better to just write your own. Or you might want to consider
using a particle swarm optimizer instead.
http://www particleswarm.info/Standard_PSO_2006.c?has source code I found
useful.
http://www.particleswarm.info/Programs.html?has lots of other implementations
to choose from
