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Applications of Learning Classifier Systems (Paperback, Softcover reprint of the original 1st ed. 2004)
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Applications of Learning Classifier Systems (Paperback, Softcover reprint of the original 1st ed. 2004)
Series: Studies in Fuzziness and Soft Computing, 150
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The field called Learning Classifier Systems is populated with
romantics. Why shouldn't it be possible for computer programs to
adapt, learn, and develop while interacting with their
environments? In particular, why not systems that, like organic
populations, contain competing, perhaps cooperating, entities
evolving together? John Holland was one of the earliest scientists
with this vision, at a time when so-called artificial intelligence
was in its infancy and mainly concerned with preprogrammed systems
that didn't learn. that, like organisms, had sensors, took Instead,
Holland envisaged systems actions, and had rich self-generated
internal structure and processing. In so doing he foresaw and his
work prefigured such present day domains as reinforcement learning
and embedded agents that are now displacing the older "standard Af'
. One focus was what Holland called "classifier systems" sets of
competing rule like "classifiers," each a hypothesis as to how best
to react to some aspect of the environment--or to another rule. The
system embracing such a rule "popu lation" would explore its
available actions and responses, rewarding and rating the active
rules accordingly. Then "good" classifiers would be selected and re
produced, mutated and even crossed, a la Darwin and genetics,
steadily and reliably increasing the system's ability to cope."
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