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In this paper, we proposed an emotional expression system as a brain-inspired system. The emotional expression was achieved by an Emotional expression Model of the Amygdala (EMA), which was an engineering model inspired by an emotional learning in the brain. EMA can realize both recognition of sensory inputs and a classical conditioning of emotional inputs. Furthermore, a specific hardware of EMA was developed with a massively parallel architecture by using an FPGA, and achieved a calculation speed that is over 20 times faster than an embedded general-purpose computer. Finally, we confirmed an effectiveness of a human-robot interaction with the emotions, which were generated by the proposed emotional expression system.
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48--54
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Bibliogr. 16 poz., rys.
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- Department of Brain Science and Engineering, Kyushu Institute of Technology, 2-4 Hibikino, Wakamatsu-ku, Kitakyushushi, 808-0196 Japan, aou@brain.kyutech.ac.jp
Bibliografia
- [1] Breazeal C.L., Designing Sociable Robots , Cambridge, MA: The MIT Press, 2002.
- [2] Fujita M., Kuroki Y., Ishida T., Doi T., “Autonomous Behavior Control Architecture of Entertainment Humanoid Robot SDR-4X”. In: Proc. of 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems , 2003, pp. 960-967.
- [2] Sawada T., Takagi T., Fujita M., “Behavior Selection and Motion Modulation in Emotionally Grounded Architecture for QRIO SDR-4X II”. In: Proc. of 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2004, pp. 2514-2519.
- [4] Sonoh S., Horio K., Aou S., Yamakawa T., “An Emotional Expression Model Inspired by the Amygdala”, International Journal of Innovative Computing, Information and Control, vol. 5, 2009, no. 5, pp. 1147-1160.
- [5] Aggleton J.P., The Amygdala: A Functional Analysis , New York: Oxford University Press, 2000.
- [6] Armony J.L., Servan-Schreiber D., Cohen J.D., LeDoux J.E., “An anatomically constrained neural network model of fear conditioning”, Behavioral Neuroscience ,vol. 109, 1995, no. 2, pp. 246-257.
- [7] Mor'en J., Balkenius C., “A computational model of emotional learning in the amygdala”. In: Proc. of the 6 th International Conference on the Simulation of Adaptive Behavior, 2000, pp. 383-391.
- [8] Phelps E.A., LeDoux J.E., “Contributions of the amygdala to emotion processing: from animal models to human behavior”, Neuron, vol. 48, 2005, pp. 175-187.
- [9] Kohonen T., Self-Organizating Maps , Berlin: Springer-Verlag, 1997.
- [10] Rescorla R.A., Wagner A.R., “A theory of Pavlovian conditioning: variations in the effectiveness of reinforcement and nonreinforcement”, Classical conditioning II: current research and theory , New York: Appleton-Century-Crofts, 1972, pp. 64-99.
- [11] Sutton R.S., “Learning to predict by the methods of temporal difference learning”, Machine Learning , vol. 3, 1988, no 1, pp. 9-44.
- [12] Cyberbotics Webots “ http://www.cyberbotics.com/”
- [13] Berglund E., Sitte J., “The parameterless self-organizing map algorithm”, IEEE Transaction on Neural Networks, vol. 17, 2006, pp. 305-316.
- [14] Tamukoh H., Horio K., Yamakawa T., “Fast Learning Algorithm for Self-Organizing Map Employing Rough Comparison WTA and its Digital Hardware Implementation”, IEICE Trans. on Electronics , vol. E87-C, 2004, no. 11, pp. 1787-1794.
- [15] Hikawa H., “FPGA implementation of self organizing map with digital phase locked loops”, Neural Networks , vol. 18, 2005, pp. 514-522.
- [16] Takemura Y., Sato M., Ishii K., “Toward Realiation of Swarm Interlligene Mobile Robots”, Brain-Inspired IT II , Amsterdam: Elsevier B.V., 2006, pp. 273-276.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BUJ7-0011-0008