Re: [computer-go] Pattern matching

[Stefan Kaitschick]

Very Cool. The mysterious part was more interesting than programming go.
That seemed almost impossible.

Stefan

- Original Message - 
From: Mark Boon tesujisoftw...@gmail.com

To: computer-go computer-go@computer-go.org
Sent: Wednesday, October 14, 2009 11:23 PM
Subject: Re: [computer-go] Pattern matching


On Sat, Oct 10, 2009 at 5:32 PM, Álvaro Begué alvaro.be...@gmail.com 
wrote:

Are you not going to tell us what this new job is about?



I almost forgot to answer this, I had no intention to sound
mysterious. My job is to make autonomous avatars (also called NPCs or
'bots') for a new MMO platform called Blue Mars. The immediate goal is
to make them more interesting, intelligent and interactive than the
zombie bots currently populating such worlds. That is a pretty easy
target. The ultimate goal is to make them actually seem intelligent
and indistinguishable from real players or even replace a real player
when he/she is not online. I've been given a practically free hand in
how I think this is accomplished best. I've only just started laying
the groundwork, but already there are some results that people are
excited about. So far so good.

Mark
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Re: [computer-go] Neural networks

[Petr Baudis]

On Wed, Oct 14, 2009 at 05:12:58PM +0200, Heikki Levanto wrote:
 On Wed, Oct 14, 2009 at 03:34:59PM +0300, Petri Pitkanen wrote:
  Neural network tend to work well in those cases where evaluation function is
  smooth, like backgammon. Even inbackgammon neural networks do give good
  results if situation has possibility of sudden equity changes like deep
  backgames and deep anchor games. Top backgammon programs 3-ply search on top
  neural network to handle these problems.
  
  I do not know wher neural nets would fit well, perhaps finding invasion
  spots?
 
 I have been speculating about a NN evaluation function for go, feeding it a
 lot of preprocessed information about the position, like number of strings
 with 1,2,3,4, or more liberties, number of stones in same, number of separate
 groups, number of obviously dead stones, strings, and groups, number of
 points clearly controlled by each player, etc, etc. This should be possible
 to train from existing games where we know the result (in the beginning it is
 50-50, in the end one or the other has won 100-0. Assume some simple function
 in between). 

Interesting idea, but I think MCTS programs already handle quite fine
the part where there is some simple function in between. Most of the
problematic games seem to be when MCTS is chasing and trying to kill a
single group, and the question is whether it succeeds; IOW, I think the
problem is tactical rather than strategical in MCTS (I have few of my
own ideas to try to deal with that, non-related to NN ;-).

I like Magnus' ideas, though the 3x3 problem seems to be a bit of a
special case of what Remi did, and I don't really have an idea how to
well train the in-UCT NN.

Another idea I received was actually not related to computers _playing_
Go, but maybe that's all the more interesting in current times - a NN
that could identify the player's playing style by looking at few of
her games and deciding which well-known player they look like (e.g.
Takemiya, Cho Chikun, Lee Sedol, TheCaptain ;-). Of course it is quite
an interesting problem what set of features to choose...

Thank you all for your ideas so far!

-- 
Petr Pasky Baudis
A lot of people have my books on their bookshelves.
That's the problem, they need to read them. -- Don Knuth
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