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The following article is from today's ComputerGram.


+    IBMer TEACHES NEURAL NETWORK TO TEACH ITSELF BACKGAMMON

IBM Corp researcher Gerald Tesauro has developed a self-teaching
programme, TD-gammon, which, with the aid of neural network
technology has learned to play backgammon well enough to beat
experts - from scratch. The network uses the temporal-difference
algorithm created by Richard Sutton of GTE Corp's GTE
Laboratories in Waltham, Massachusetts, which is a kind of
delayed reinforcement mechanism. Tesauro's program runs on an
RS/6000 workstation, and sets out the rules and objectives of
the game and the layout of the board; the machine is then left
to develop its strategy, through trial and error, by playing
against itself. It assesses the viability of each potential move
against the values stored in the algorithm; these values are
adjusted after each game, either upwards when it wins or
downwards when it loses - a process that leads to significant
improvements in performance after only 100 games. The RS/6000
enables some 300,000 games to be played in a month - vital
because of the mere 25,000 synapses contained in the Tesauro
program, which is equivalent in complexity to the brain of a
slug. The game been declared one of the best by expert
backgammon players - the machine recently achieved a 19-19 draw
against experts at the World Backgammon Cup tournament. In
addition, Tesauro claims it has inspired research into the
application of similar techniques to automatic-control problems,
such as guiding robot hands and arms, and to prediction
problems, such as forecasting trends in the behaviour of
financial markets, and has thrown light on human and mechanical
learning processes.