Intelligent exploration method for XCS | Zendy

Ali Hamzeh | Zendy; Adel Torkaman Rahmani | Zendy

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Intelligent exploration method for XCS

Author(s) -

Ali Hamzeh,

Adel Torkaman Rahmani

Publication year - 2005

Publication title -

citeseer x (the pennsylvania state university)

Language(s) - English

Resource type - Conference proceedings

DOI - 10.1145/1102256.1102279

Subject(s) - computer science , reinforcement learning , artificial intelligence , benchmark (surveying) , machine learning , intelligent control , classifier (uml) , learning classifier system , evolutionary computation , geodesy , geography

Exploration/Exploitation equilibrium is one of the most challenging issues in reinforcement learning area as well as learning classifier systems such as XCS. In this paper, an intelligent method is proposed to control exploration rate in XCS to improve its long-term performance. This method is called Intelligent Exploration Method (IEM) and is applied to some benchmark problems to show advantages of adaptive exploration rate for XCS. It

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