Conv net should be robust. From image processing domian, these are feature
detectors (shape in case of go) that are invariant to translations (moving
shape left right up down along board). Enlarging board wouldnt put bot at
 disadvantage in evaluating local positions.


On Tuesday, March 22, 2016, Ray Tayek <rta...@ca.rr.com> wrote:

> On 3/22/2016 5:21 PM, Lukas van de Wiel wrote:
>
> It would reduce Alphago, because there is less training material in the
> form of high-dan-games, to train the policy network.
>
> It would also reduce the skill of a human opponent, because (s)he would
> have less experience on a larger board, just as AlphaGo.
>
> It would be fun to see which can adapt better.
>
>
> human would adapt quickly after a few games (say 10 or so).
>
> thanks
>
>
> On Wed, Mar 23, 2016 at 1:18 PM, Ray Tayek <rta...@ca.rr.com
> <javascript:_e(%7B%7D,'cvml','rta...@ca.rr.com');>> wrote:
>
>> On 3/22/2016 11:25 AM, Tom M wrote:
>>
>>> I suspect that even with a similarly large training sample for
>>> initialization that AlphaGo would suffer a major reduction in apparent
>>> skill level.
>>>
>> i think a human would also.
>>
>>>    The CNN would require many more layers of convolution;
>>> the valuation of positions would be much more uncertain; play in the
>>> corner, edges, and center would all be more complicated patterns, and
>>> there would be far more good candidates to consider at each ply and
>>> rollouts would be much less stable and less accurate.
>>>
>> yes.
>>
>> the normal board size is 19x19 because the amount of territory in the
>> sides and corners is slightly larger than the amount of territory in the
>> middle.
>>
>> thanks
>>
>> --
>> Honesty is a very expensive gift. So, don't expect it from cheap people -
>> Warren Buffett
>> http://tayek.com/
>>
>>
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>
>
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>
> --
> Honesty is a very expensive gift. So, don't expect it from cheap people - 
> Warren Buffetthttp://tayek.com/
>
>

-- 
Yuancheng [Mike] Luo
www.umiacs.umd.edu/~yluo1 <http://www.cs.umd.edu/%7Eyluo1>
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