Designing reactive emotion generation model for interactive robots

Author(s):  
Hyoung-Rock Kim ◽  
Seong-Yong Koo ◽  
Dong-Soo Kwon
2013 ◽  
Vol 461 ◽  
pp. 618-622
Author(s):  
Chuan Wan ◽  
Yan Tao Tian

Affective computing is an indispensable aspect in harmonious human-computer interaction and artificial intelligence. Making computers have the ability of generating emotions is a challenging task of affective computing. Affective Computing and Artificial Psychology are new research fields that involve computer and emotions, they have the same key research aspect, affective modeling. The paper introduces the basic affective elements, and the representation of affections in a computer. And we will describe an emotion generation model for a multimodal virtual human. The relationship among the emotion, mood and personality are discussed, and the PAD emotion space is used to define the emotion and the mood. We obtain the strength information of each expression component through fuzzy recognition of facial expressions based on Ekman six expression classifications, and take this information as a signal motivating emotion under the intensity-based affective model. Finally, a 3D virtual Human head with facial expressions is designed to show the emotion generation outputs. Experimental results demonstrate that the emotion generation intensity-based model works effectively and meets the basic principle of human emotion generation.


Author(s):  
Miho Harata ◽  
◽  
Masataka Tokumaru ◽  

In this paper, we propose an emotion model with growth functions for robots. Many emotion models for robots have been developed using Neural Networks (NN), which focus on the functions of emotion recognition, control, and expression. One problem that affects these emotion models for robots is the development of a “simplified” emotion generation algorithm. Users readily lose interest in “simple” systems. Most models have attempted to generate complex emotional expressions, whereas no previous studies have considered the “growth of a robot.” Therefore, we propose a growth model for emotions based on changes in the network structure of a self-organizing map. We also applied a multilayer perceptron NN to generate more sophisticated expressions of emotion using growth functions. This model generated a similar behavior to the concept of affective change described in genetic psychology. Our results showed that this emotion model was more suitable for producing a robot with growth functions based on a psychological model.


2007 ◽  
Vol 25 (7) ◽  
pp. 1125-1133 ◽  
Author(s):  
Shogo Takeuchi ◽  
Ayumi Sakai ◽  
Shohei Kato ◽  
Hidenori Itoh

2010 ◽  
Vol 38 (1/2/3) ◽  
pp. 212
Author(s):  
Chao gang Wan ◽  
Jie yu Zhao ◽  
Yuan yuan Zhang

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