against each other, given that :
- My players dont know anything about the suicide and the ko rule.
- Thay can't count the score by themselves.
Ernest Galbrun
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against each other, given that :
- My players dont know anything about the suicide and the ko rule.
- Thay can't count the score by themselves.
Ernest Galbrun
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>
> I am not sure what you mean by "the result of a game".
>
> If a game has stopped because two weak players have passed in turn, then
> "the result" may, depending on the rules used, be undefined, or difficult or
> inappropriate to calculate. If a game has stopped because two expert
> players ha
Well, this is precisely what I was looking for, thank you very much.
Ernest Galbrun
On Sun, Jan 11, 2009 at 17:23, Ben Shoemaker wrote:
> Ernest,
>
> If your players support GTP, you can automate playing two gtp engines
> against each other using the twogtp script that comes wi
blog : http://goia-hephaestos.blogspot.com/
Ernest Galbrun
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> How do you perform the neuro-evolution? What sort of genetic
> operators do you have? Do you have any sort of crossover? How do you
> represent the board and moves to the networks?
>
> - George
- The evolution consists in the random mutation of each neurons : weight,
type of neurone, thresho
On Fri, Feb 13, 2009 at 22:42, Mark Boon wrote:
> Just curious, did you ever read 'On Intelligence' by Jeff Hawkins? After
> reading that I got rather sold on the idea that if you're ever going to
> attempt making a program with neural nets that behaves intelligently then it
> needs to have a lot
> Nice project!
>
> I worked on this some time ago. I did not use neural networks but patterns
> with feedback.
>
> The problem with feedback is that it is difficult to know when it reaches
> its final state. Usually you get oscillations and that state never happens.
>
> I tried to solve that using
>
> I read a paper a couple years ago about a genetic algorithm to evolve
> a neural network for Go playing (SANE I think it was called?). The
> network would output a value from 0 to 1 for each board location, and
> the location that had the highest output value was played as the next
> move. I
ction to duplicate and be
used elsewhere through the definition of genes in my neural network. The
players will have to find out how to use this. And yes, I intend the players
to find by themselves about the simplest go principle, I think this is what
evolution is best at (you now, actually evol
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