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Abstract:
Classifier systems are now viewed disappointing because of
their problems such as the rule strength vs rule set performance
problem and the credit assignment problem. In order to solve the
problems, we have developed a hybrid classifier system: GLS
(Generalization Learning System). In designing GLS, we view
classifier systems as model free learning in POMDPs and take a
hybrid approach to finding the best generalization, given the total
number of rules. GLS uses the policy improvement procedure by
Jaakkola et al. for the optimal stochastic policy when a set of
rule conditions is given. GLS uses GA to search for the best set of
rule conditions.
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