Study on an Emotion Generation Model for a Robot Using a Chaotic Neural Network

Abstract : This paper proposes an emotion-generation model for complex change using a chaotic neural network (CNN). Using a CNN, the proposed model will solve the problem of past studies that have indicated that robotic emotion changes are simplistic. The model uses the principle of an adaptation level, which is used in Russell's emotion model to generate emotion. This paper considers the effectiveness of this approach using simulation, and shows that the model can express a change of "adaptation". In addition, through the chaos of CNN, the proposed model can express different changes, even if the values of CNN's input values remain the same.
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Communication dans un congrès
Hyun Seung Yang; Rainer Malaka; Junichi Hoshino; Jung Hyun Han. 9th International Conference on Entertainment Computing (ICEC), Sep 2010, Seoul, South Korea. Springer, Lecture Notes in Computer Science, LNCS-6243, pp.502-504, 2010, Entertainment Computing - ICEC 2010. 〈10.1007/978-3-642-15399-0_73〉
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Hiroyuki Sumitomo, Masataka Tokumaru, Noriaki Muranaka. Study on an Emotion Generation Model for a Robot Using a Chaotic Neural Network. Hyun Seung Yang; Rainer Malaka; Junichi Hoshino; Jung Hyun Han. 9th International Conference on Entertainment Computing (ICEC), Sep 2010, Seoul, South Korea. Springer, Lecture Notes in Computer Science, LNCS-6243, pp.502-504, 2010, Entertainment Computing - ICEC 2010. 〈10.1007/978-3-642-15399-0_73〉. 〈hal-01055587〉

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