Date: Mon, 01 Mar 1999 15:53:09 +1030
From: Luke Pellen <luke-AT-seol.net.au>
Subject: Just so you know what I'm up to...


How does an ANN [artificial neural network] work?
-------------------------------------------------

  An ANN is an implementation of "connectionist" architecture. A connectionist
architecture seeks to loosely emulate the workings of a biological brain and is
characterized by having a large number of very simple neuron-like processing
elements, a large number of weighted connections between these elements,
parallel distributed control, and an emphasis on learning internal
representations automatically. The weights connecting each neuron encode the
knowledge of the network.

  A simulated neuron is a node connected to other nodes via links that
approximate to axon-synapse-dendrite connections. Each link is associated with
a weight. The connecting weight multiplied by the neuron's output determines
the nature and strength of one node's influence on another: a large positive
weight corresponds to strong excitation, and a small negative weight
corresponds to weak inhibition.


How does Octavius work?
-----------------------

  Octavius is classified as a feed-forward network: he has 768 input nodes, 20
hidden nodes, and 16 output nodes. Each input node is linked to each hidden
node, and each hidden node is linked to each output node. This gives Octavius a
total of 804 nodes and 15680 individually weighted connections.

  A chess position is encoded as binary and fed into the input neurons. The
reason for 768 input nodes is that there are 64 squares on a chess board, and
each square has 12 possible states [my pawn, enemy pawn, my bishop, enemy
bishop etc.]. An empty square is simply represented as 12 zeros.

  The hidden layer consists of 20 nodes; this number is purely arbitrary and
subject to change pending further experimentation. The hidden layer is where
the bulk of the processing is performed.

  The output layer consists of 16 nodes which, again, is an arbitrary figure
essentially allowing Octavius 16 levels of judgment. The raw values of these
output nodes are totaled to give a numerical positional evaluation.


How does Octavius learn?
------------------------

  A human who learns how to play chess begins with the basics: the movements of
the pieces, the relative values of the pieces, threats, traps and discovered
checks etc. 
  
  Octavius attempts to gain an understanding of chess through a positional
analysis of master and grand master games. His training is based on the
assumption that any position reached during such games must be positionally
superior to any alternatively available position during that game. 

  It must be stressed that Octavius has absolutely no hard coded knowledge of
chess. My theory is that through exposure to master and grand master games
Octavius will be able to deduce the rules and tactics of chess heuristically
via positional interpolation. This top-down method is precisely the reverse of
the human bottom-up approach. 

  His performance to date has been close to that of a very bad human chess
player. Here is one of his best games against me (he starts quite well, but
deteriorates as the game progresses):

  White: Luke Pellen
  Black: Octavius

  1. e4 d5
  2. exd5 e5
  3. Nc3 Nf6
  4. d3 e4
  5. dxe4 Bb4
  6. Bd2 Bxc3
  7. Bxc3 Nxe4
  8. Nf3 Nf6 
  9. Be2 Nxd5
 10. O-O Ne3 	(nice try Octavius... )
 11. fxe3 Bf5 
 12. Nd4 c5 
 13. Nxf5 c4
 14. Nxg7+ Ke7 
 15. Bh5 f5 
 16. Nxf5+ Ke6 
 17. Qg4 b5 
 18. Rad1 b4 
 19. Ng7+ Ke7
 20. Rf7#
 {White mates} 1-0



Luke Pellen
e-mail: luke-AT-seol.net.au
ICQ#: 25510475
-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
For in and out, above, about, below,
'Tis nothing but a Magic Shadow-show
Play'd in a Box whose Candle is the Sun,
Round which we Phantom Figures come and go.

- The Rubaiyat Of Omar Khayyam
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Chaotic Pearl:  http://members.tripod.com/~vidagnosis/journal.html

This random quotation was generated by SIGGEN...
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