Dave,
Thank you for taking the time giving me these advices. I will give you my
opinion about your last point first, beacause I think it is the most
important point, stressing out what I really wish to achieve with this
project. I am perfectly aware that I am very naive and bold in my approach
to t
an artificial go player through random mutation and natural selection
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
On Tue, Feb 24, 2009 at 08:40, Daniel Burgos wrote:
> 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 happe
On Feb 24, 2009, at 4:40 AM, Daniel Burgos wrote:
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 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
> 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
On Tue, Feb 24, 2009 at 2:40 AM, Daniel Burgos wrote:
> 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 th
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 timeouts, but
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
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 of feed-back links. Not just
the standard feed-fo
> 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
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
On Fri, Feb 13, 2009 at 2:42 PM, Ernest Galbrun
wrote:
> Hello,
> I would like to share my project with you
Hello,
I would like to share my project with you : I have developped a program
trying to mimic evolution through the competition of artificial go players.
The players are made of totally mutable artificial neural networks, and the
compete against each other in a never ending tournament, randomly mu
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