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From: mahler@latcs1.oz.au (Daniel Mahler)
Newsgroups: comp.ai.neural-nets
Subject: Re: A neural net who plays chess
Message-ID: <8966@latcs1.oz.au>
Date: 10 Oct 90 08:46:46 GMT
References: <6758@uwm.edu> <62519@iuvax.cs.indiana.edu> <8954@latcs1.oz.au>
Reply-To: mahler@latcs1.oz.au (Daniel Mahler)
Organization: Comp Sci, La Trobe Uni, Australia
Lines: 39


	Several people have pointed out
that my claim about trivial fixpoints
is not true as your program should recognise
terminal game positions and give absolute
value to these.  You of course have to do this to give
bp some goal directed bias, but for a game of any complexity
there will be only few terminal nodes in your search horizon.
There will usually be some in chess but NONE
in go or othello until the endgame.
	For noughts and crosses we can overcome all the problems
by tabulating all the legal positions and their true min-max values.
This is feasible as there are less than 3^9 legal positions
to evaluate. It happens in a few seconds of real-time.
Then you do not have to worry about using genetics
to avoid inbred strategies, but you do not have to
worry about neural nets or learning either.
	I gave my tic-tac-toe program a limited
horizon because I was intersted in going onto go
(bravery is usually born of innocence/ignorance).
	Overall, I do not think this generalised
Samuel's technique is viable for learning from scratch,
which is what machine learning people really want. :)
(For this Lenat's Eurisco, genetics or Barto&Sutton seem more promising)
First it is computationally
very expensive: to produce the teaching signal
you must do a search and eavaluate a neural net
at each terminal node. Initially this signal will be poor
anyway. It is likely that tree pathology (Nau's work)
becomes a significant factor under these circumstances.
Also for a complex game the net must
learn to cluster positions by tactical/stragic concepts
from the extremely small percentage of all legal positions
it will ever see. 
	However, a slight modification of Samuel's idea seems good.
Use feature detectors designed by experts as inputs to a small net.
This will then be Samuels method extended to allow nonlinear combinations
of these features as evaluation functions.
	Daniel