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Journal of Robotics
Volume 2010 (2010), Article ID 241785, 13 pages
HCBPM: An Idea toward a Social Learning Environment for Humanoid Robot
1Department of System Design Engineering, Graduate School of Engineering, University of Fukui, Fukui 910-8507, Japan
2Department of Human and Artificial Intelligence Systems, University of Fukui, Fukui 910-8507, Japan
3Research and Education Program for Life Science, University of Fukui, Fukui 910-8507, Japan
Received 3 March 2010; Revised 1 July 2010; Accepted 2 July 2010
Academic Editor: Danica Kragic
Copyright © 2010 Fady Alnajjar et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
- D. Floreano and C. Mattiussi, Bio-Inspired Artificial Intelligence: Theories, Methods, and Technologies, The MIT Press, Cambridge, Mass, USA, 2008.
- A. R. Hafiz, F. Alnajjar, and M. Kazuyuki, “A new dynamic edge detection toward better human-robot interaction,” in Proceedings of the 12th FIRA Robot World Congress on Advances in Robotics, J. H. Kim, S. S. Ge, P. Vadakkepat, N. Jesse, et al., Eds., vol. 5744 of Lecture Notes in Computer Science, pp. 44–52, Springer, Incheon, Korea, August 2009.
- A. R. Hafiz, F. Alnajjar, and M. Kazuyuki, “A novel dynamic edge detection inspired from mammalian retina toward better robot vision,” in World Automation Congress, Kobe, Japan, September 2010, in Press.
- D. Floreano, J. Zufferey, and J.-D. Nicoud, “From wheels to wings with evolutionary spiking circuits,” Artificial Life, vol. 11, no. 1-2, pp. 121–138, 2005.
- J. McClelland, “How far can you go with Hebbian learning, and when does it lead you astray?” in Attention and Performance XXI: Processes of Change in Brain and Cognitive Development, Y. Munakata and M. H. Johnson, Eds., Oxford University Press, 2005.
- S. Schaal, “Is imitation learning the route to humanoid robots?” Trends in Cognitive Sciences, vol. 3, no. 6, pp. 233–242, 1999.
- D. C. Bentivegna, C. G. Atkeson, A. Ude, and G. Cheng, “Learning to act from observation and practice,” International Journal of Humanoid Robotics, vol. 1, no. 4, pp. 585–611, 2004.
- S. H. Frey and V. E. Gerry, “Modulation of neural activity during observational learning of actions and their sequential orders,” Journal of Neuroscience, vol. 26, no. 51, pp. 13194–13201, 2006.
- W. Gordon, Learning Through Interaction, Cambridge University Press, Cambridge, UK, 1981.
- A. Billard and R. Siegwart, “Robot learning from demonstration,” Robotics and Autonomous Systems, vol. 47, no. 2-3, pp. 65–67, 2004.
- D. Kulić and Y. Nakamura, “Incremental learning and memory consolidation of whole body human motion primitives,” Adaptive Behavior, vol. 17, no. 6, pp. 484–507, 2009.
- T. Ogata, S. Sugano, and J. Tani, “Open-end human-robot interaction from the dynamical systems perspective: mutual adaptation and incremental learning,” Advanced Robotics, vol. 19, no. 6, pp. 651–670, 2005.
- M. Islam, A. Sattar, F. Amin, X. Yao, and K. Murase, “A new adaptive merging and growing algorithm for designing artificial neural networks,” IEEE Transactions on Systems, Man, and Cybernetics, Part B, vol. 39, no. 3, pp. 705–722, 2009.
- A. D. Baddeley, Essentials of Human Memory, Psychology Press, London, UK, 1999.
- D. E. J. Linden, R. A. Bittner, and R. A. Bittner, “Cortical capacity constraints for visual working memory: dissociation of fMRI load effects in a fronto-parietal network,” NeuroImage, vol. 20, no. 3, pp. 1518–1530, 2003.
- F. Edin, T. Klingberg, P. Johansson, F. McNab, J. Tegnér, and A. Compte, “Mechanism for top-down control of working memory capacity,” Proceedings of the National Academy of Sciences of the United States of America, vol. 106, no. 16, pp. 6802–6807, 2009.
- M. Likhachev, M. Kaess, and R. C. Arkin, “Learning behavioral parametrization using spatio-temporal case-based reasoning,” in Proceedings of the IEEE International Conference on Robotics and Automation, vol. 2, pp. 1282–1289, May 2002.
- N. Gunaseeli and N. Karthikeyan, “A constructive approach of modified standard backpropagation algorithm with optimum initialization for feedforward neural networks,” in International Conference on Computational Intelligence and Multimedia Applications (ICCIMA '07), pp. 325–331, December 2007.
- F. Alnajjar, I. B. M. Zin, and K. Murase, “A hierarchical autonomous robot controller for learning and memory: adaptation in a dynamic environment,” Adaptive Behavior, vol. 17, no. 3, pp. 179–196, 2009.
- L. Fu, H.-H. Hsu, and J. C. Principe, “Incremental backpropagation learning networks,” IEEE Transactions on Neural Networks, vol. 7, no. 3, pp. 757–761, 1996.
- M. Lehtokangas, “Modelling with constructive backpropagation,” Neural Networks, vol. 12, no. 4-5, pp. 707–716, 1999.
- S.-U. Guan and S. Li, “Incremental learning with respect to new incoming input attributes,” Neural Processing Letters, vol. 14, no. 3, pp. 241–260, 2001.
- T.-Y. Kwok and D.-Y. Yeung, “Objective functions for training new hidden units in constructive neural networks,” IEEE Transactions on Neural Networks, vol. 8, no. 5, pp. 1131–1148, 1997.
- M. A. McDaniel, “Big-brained people are smarter: a meta-analysis of the relationship between in vivo brain volume and intelligence,” Intelligence, vol. 33, no. 4, pp. 337–346, 2005.
- S. Barbara, “Understanding How the Brain Learns, What Learning Does to Your Brain,” 2004, http://www.nichcy.org/EducateChildren/effective/pages/brain101.aspx.