[EMAIL PROTECTED]: <[EMAIL PROTECTED]>:
>> delta_komi = 10^(K * (number_of_empty_points / 400 - 1)),
>> where K is 1 if winnig and is 2 if loosing. Also, if expected
>> winning rate is between 45% and 65%, Komi is unmodified.
>
>There's one thing I don't like at all, there: you could get positive
On Thu, Feb 28, 2008 at 01:01:08PM -0500, Don Dailey wrote:
> It's naive to think some simplistic deception imposed upon the program
> is going to correct the error when you don't even know if the program is
> erring in the first place. How can you say, "the program thinks it
> is losing, but
> delta_komi = 10^(K * (number_of_empty_points / 400 - 1)),
> where K is 1 if winnig and is 2 if loosing. Also, if expected
> winning rate is between 45% and 65%, Komi is unmodified.
There's one thing I don't like at all, there: you could get positive
evaluation when losing, and hence play conser
On Mon, Feb 18, 2008 at 09:03:17PM +0100, Alain Baeckeroot wrote:
> Le lundi 18 février 2008, Michael Williams a écrit :
> > But as was pointed out before, these high levels of MoGo are probably still
> > not pro level, right?
> >
>
> On 9x9 Big_slow_Mogo is near pro level, maybe more.
> 6 month
> http://ewh.ieee.org/cmte/cis/mtsc/ieeecis/tutorial2007/Bruno_Bouzy_2007.pdf
>
> Page 89, "which kind of outcome". This method is better than the above
> and similar to what Jonas seems to propose. The improvement is minor.
By looking at their proposal (45 * win + score), in contrast to mine,
th
Don Dailey wrote:
I personally have serious doubts about knowledge extraction from human
games, but I hope you have success.I think you can get more from
computer games of strong players even though the level is weaker. Here
is why I say that:
1. A strong computer still plays a lot of go
