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Re: [Bug-gnubg] Training neural nets: How does size matter?

From: Douglas Zare
Subject: Re: [Bug-gnubg] Training neural nets: How does size matter?
Date: Sun, 1 Sep 2002 04:31:17 -0400
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Quoting Joern Thyssen <address@hidden>:

> On Fri, Aug 30, 2002 at 09:56:28AM +0200, Øystein O Johansen wrote
> > Hi,
> > 
> > gnubg uses a race network, a contact network and a crashed network.
> > I think these scheme works OK. The crashed network is a not mature
> > yet, but there is work in progress. There has also been discussions
> > about splitting into more classes. I guess this will be done
> > eventually, but we must take one step at the time. Two years ago
> > there was also a network called BPG, (Backgame and Prime). There
> > was really some problems with this net, so is was removed.
> As far as I remember the problem back then was that a position could be
> BPG, then turn into contact, and back into BPG. 

That does make it more difficult to bootstrap, but was that the real problem?

> There is discontinuities are the boundaries between two neural nets,
> i.e., the evaluate the same position differently. For example, that may
> lead to the program refusing to break contact, as the contact neural net
> overestimates the contact position. As I remeber it the big problem with
> the BPG net was huge discontinuities at the boundaries.
> The conclusion was that we should avoid "loops". For example, currently
> we have:
> Contact -----------  Race   ------ Bearoff
>     \               /
>      \__ Crashed __/
> so no loops. Another way to avoid the discontinuities is the meta-pi
> scheme, since this will make the evaluations continuous. The price is
> that you, in general, have to evaluate two neural nets.

Would two neural nets and a meta-pi system be better than one neural net of 
twice the size? I don't see the advantage, abstractly, although I can imagine 
that one would mainly focus on the race in a midpoint vs. midpoint contact 
position. (On the other hand, Walter Trice mentioned a very interesting 
midpoint vs. midpoint position that would be a big pass due to the race, but 
was a take due to the pips wasted on repeated x-1's.) 

I call the discontinuities "blemishes" after Boyan. I think both humans and 
neural nets face a problem related to blemishes when considering radically 
different positions that can result from playing doubles in different ways. It 
hardly matters if the contact net is used to evaluate both a blitz and an 
outside prime, as there is little reason for the evaluations to be consistent. 
One solution I try in my own play is to be able to produce better absolute 
evaluations, e.g., "After this move, am I the favorite? Should I accept if my 
opponent offers me a point on a 2-cube?" This has prevented a few massive 
blunders, and I think it is actually a strength of bots, not a weakness. 

How did you try to find a representative set of data to train the backgame net? 
Is there an archive of the discussions?

Douglas Zare

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