Artificial Neural Networks might be remotely inspired by something
biological, but I don't think anyone expects them to "think like a human
player". I am not sure how one would use an "Ant Colony Optimization
Algorithm" to play go; but wouldn't those think like ant hives, not humans?
:)

Of course, the fact that people are trying to use biologically inspired
solutions must mean that it is the way to go. As history has taught us:
https://www.youtube.com/watch?v=gN-ZktmjIfE

Enjoy! :)

Álvaro.




On Mon, Apr 1, 2013 at 1:42 PM, Gabriel .Santos <[email protected]>wrote:

> Álvaro,
>
> When I say "think like a human player ", I mean regarding to the strategy.
> For example, when there are several fights happening simultaneously at the
> board, a human player can identify them and decide which one worth more to
> invest, I thinks this is a really difficult task in Go. How does he do this
> judge ?  Which features does he analyze? And there are cases which "try" to
> mimic the biological solution is worth. See Neural Networks, Ant Colony
> Optimization Algorithm, Genetic Algorithm, etc.
>
> Santos, Gabriel.
>
>
> On Mon, Apr 1, 2013 at 2:30 PM, Álvaro Begué <[email protected]>wrote:
>
>>
>> On Mon, Apr 1, 2013 at 11:10 AM, Gabriel .Santos <
>> [email protected]> wrote:
>>
>>> I know that it is a lot of questions, but in order to get a computer go
>>> machine to outperform a human player I think that the machine should to
>>> ratiocinate like a human player.
>>
>>
>>
>> Do you also think a machine that carries people very fast should have
>> strong legs like a horse? And a machine that can fly should flap its wings
>> like a bird? And a closer example: Do you think the same thing about chess
>> machines?
>>
>> In all those cases the engineering solution to the problem was very
>> different from the biological solution, and I expect the same will happen
>> with computer go. Actually, it's already happening.
>>
>> Álvaro.
>>
>>
>>
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>
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