Yeah, I would expect that encoding stones as "signed liberty count" would train 
faster/better/stronger. You could imagine a follow-up paper where Go features 
are supplied.

But, if I think about this on a large scale, wouldn't it be huge to put 
together a general game-playing program just based on visual-NN and simple 
MCTS? In the extended section is a comparison of 
checkers/backgammon/poker/scrabble(thanks!)/... that have been addressed by 
rollouts, and how those research efforts differed from the tabula rasa / ab 
initio formulation. The list of games that are played above human caliber now 
includes deterministic and non-deterministic, and perfect and imperfect 
information, so there are examples of every type out there.

I'm hoping that DeepMind will pursue general game-playing. It would be awesome 
to see strong AIs emerging from first principles. I would be happy to supply 
Maven as a sparring partner for Scrabble. I also have a world-class Pineapple 
Poker player that is based on rollouts and hand-coded features.

-----Original Message-----
From: Computer-go [mailto:computer-go-boun...@computer-go.org] On Behalf Of 
Gian-Carlo Pascutto
Sent: Wednesday, October 18, 2017 4:38 PM
To: computer-go@computer-go.org
Subject: Re: [Computer-go] AlphaGo Zero

On 18/10/2017 22:00, Brian Sheppard via Computer-go wrote:
> A stunning result. The NN uses a standard vision architecture (no Go 
> adaptation beyond what is necessary to represent the game state).

The paper says that Master (4858 rating) uses Go specific features, initialized 
by SL, and the same technique. Without go features, and without initialization, 
it's Zero (5185 rating).

The obvious question is, what would be the result of using go features and not 
initializing?

I would expect that providing liberties is a useful shortcut (see my remark 
about game history!). But I'm willing to be surprised :-)

--
GCP
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