Seminar on Computational Learning and
Adaptation
Experience-Based
Chess Play
Robert Levinson
Department of Computer Science
University of California, Santa
Cruz
We begin by reviewing the state of the art in computer chess and game
playing in general. Despite the obvious success of chess computers,
which compete on a level approximately equal to the best humans, one
can
always construct chess positions in which the computer is almost
clueless.
Moreover, typical programs do not grasp the underlying structure of
chess, and human chess players' use of patterns and learning is
obviously more
efficient. We present an alternative architecture, Morph, that
incorporates
technologies such as neural networks, genetic algorithms, graph
matching,
and representation change and that, with little human assistance, goes
from random chess play to a rating over 1500, equivalent to beating 45
percent of tournament players. Although our combination and
understanding
of these technologies (and chess) is far from perfect, we believe the
future is bright for their use in complex domains. The talk is suitable
for a general computer science audience.
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Date: Wednesday, April 6, 2005
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Time: 4:15-5:30PM
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Place: Gates 104
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