人工知能
Online ISSN : 2435-8614
Print ISSN : 2188-2266
人工知能学会誌(1986~2013, Print ISSN:0912-8085)
POMDP研究に基づいたハイブリッド分類子システム
林 朗末松 伸朗
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解説誌・一般情報誌 フリー

1999 年 14 巻 3 号 p. 538-546

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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 CSs 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 Jackal 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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© 1999 人工知能学会
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