A cooperative framework of learning automata and its application in tutorial-like system

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• 作者：Hao Ge^a ; ^{sjtu_gehao@sjtu.edu.cn" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Yifan Wang^a ; ^{wangyifan_1123@sjtu.edu.cn" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Shenghong Li^a ; ^{shli@sjtu.edu.cn" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Chun Lung Philip Chen^b ; ^{philipchen@umac.mo" class="auth_mail" title="E-mail the corresponding author} ; Ying Guo^a ; ^{guoying2014vip@sjtu.edu.cn" class="auth_mail" title="E-mail the corresponding author}
• 关键词：Tutorial-like system ; Learning automata (LA) ; Cooperative method ; Current State of Learning (SoL)
• 刊名：Neurocomputing
• 出版年：2016
• 出版时间：5 May 2016
• 年：2016
• 卷：188
• 期：Complete
• 页码：311-318
• 全文大小：677 K

文摘

A novel cooperative framework of learning automata (LA) is presented in this paper. In a system, where different LA can be integrated via the proposed framework, current State of Learning (SoL) index is advocated to evaluate the learning status of individual LA. Based on that learning status, individual LA will adaptively choose an appropriate interaction strategy at cooperative learning phase. Theoretical analysis demonstrated that this index is able to preserve the ε-optimal feature of independent learning automata. Experimental simulations validated that this cooperative framework is effective in improving learning speed of a variety of LA embedded in this framework. Then, we also present an application of the proposed cooperative framework in tutorial-like systems. Compared with existing method, our cooperative framework based method outperforms in both speed and accuracy.