- About this Journal ·
- Abstracting and Indexing ·
- Advance Access ·
- Aims and Scope ·
- Article Processing Charges ·
- Articles in Press ·
- Author Guidelines ·
- Bibliographic Information ·
- Citations to this Journal ·
- Contact Information ·
- Editorial Board ·
- Editorial Workflow ·
- Free eTOC Alerts ·
- Publication Ethics ·
- Reviewers Acknowledgment ·
- Submit a Manuscript ·
- Subscription Information ·
- Table of Contents
Applied Computational Intelligence and Soft Computing
Volume 2012 (2012), Article ID 354785, 11 pages
Multilevel Cognitive Machine-Learning-Based Concept for Artificial Awareness: Application to Humanoid Robot Awareness Using Visual Saliency
Images, Signals and Intelligence Systems Laboratory (LISSI/EA 3956) and Senart-FB Institute of Technology,
University Paris-EST Créteil (UPEC), Bât.A, avenue Pierre Point, 77127 Lieusaint, France
Received 11 March 2012; Revised 12 May 2012; Accepted 20 May 2012
Academic Editor: Qiangfu Zhao
Copyright © 2012 Kurosh Madani 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.
- E. R. Westervelt, G. Buche, and J. W. Grizzle, “Experimental validation of a framework for the design of controllers that induce stable walking in planar bipeds,” International Journal of Robotics Research, vol. 23, no. 6, pp. 559–582, 2004.
- J. H. Park and O. Kwon, “Reflex control of biped robot locomotion on a slippery surface,” in Proceedings of the IEEE International Conference on Robotics and Automation (ICRA '01), pp. 4134–4139, May 2001.
- J. Chestnutt and J. J. Kuffner, “A tiered planning strategy for biped navigation,” in Proceedings of the 4th IEEE-RAS International Conference on Humanoid Robots (Humanoids '04), vol. 1, pp. 422–436, November 2004.
- Q. Huang, K. Yokoi, S. Kajita et al., “Planning walking patterns for a biped robot,” IEEE Transactions on Robotics and Automation, vol. 17, no. 3, pp. 280–289, 2001.
- K. Sabe, M. Fukuchi, J. S. Gutmann, T. Ohashi, K. Kawamoto, and T. Yoshigahara, “Obstacle avoidance and path planning for humanoid robots using stereo vision,” in Proceedings of the IEEE International Conference on Robotics and Automation (ICRA '4), pp. 592–597, May 2004.
- R. Holmes, Acts of War: The Behavior of Men in Battle, The Free Press, New York, NY, USA, 1st American edition, 1985.
- M. Tambe, W. L. Johnson, R. M. Jones et al., “Intelligent agents for interactive simulation environments,” AI Magazine, vol. 16, no. 1, pp. 15–40, 1995.
- P. Langley, “An abstract computational model of learning selective sensing skills,” in Proceedings of the 18th Conference of the Cognitive Science Society, pp. 385–390, 1996.
- C. Bauckhage, C. Thurau, and G. Sagerer, “Learning human-like opponent behavior for interactive computer games,” Lecture Notes in Computer Science, vol. 2781, pp. 148–155, 2003.
- V. Potkonjak, D. Kostic, S. Tzafestas, M. Popovic, M. Lazarevic, and G. Djordjevic, “Human-like behavior of robot arms: general considerations and the handwriting task—part II: the robot arm in handwriting,” Robotics and Computer-Integrated Manufacturing, vol. 17, no. 4, pp. 317–327, 2001.
- J. Edlund, J. Gustafson, M. Heldner, and A. Hjalmarsson, “Towards human-like spoken dialogue systems,” Speech Communication, vol. 50, no. 8-9, pp. 630–645, 2008.
- A. Lubin, N. Poirel, S. Rossi, A. Pineau, and O. Houdé, “Math in actions: actor mode reveals the true arithmetic abilities of french-speaking 2-year-olds in a magic task,” Journal of Experimental Child Psychology, vol. 103, no. 3, pp. 376–385, 2009.
- F. A. Campbell, E. P. Pungello, S. Miller-Johnson, M. Burchinal, and C. T. Ramey, “The development of cognitive and academic abilities: growth curves from an early childhood educational experiment,” Developmental Psychology, vol. 37, no. 2, pp. 231–242, 2001.
- G. Leroux, M. Joliot, S. Dubal, B. Mazoyer, N. Tzourio-Mazoyer, and O. Houdé, “Cognitive inhibition of number/length interference in a Piaget-like task in young adults: evidence from ERPs and fMRI,” Human Brain Mapping, vol. 27, no. 6, pp. 498–509, 2006.
- A. Lubin, N. Poirel, S. Rossi, C. Lanoë, A. Pineau, and O. Houdé, “Pedagogical effect of action on arithmetic performances in Wynn-like tasks solved by 2-year-olds,” Experimental Psychology, vol. 57, no. 6, pp. 405–411, 2010.
- O. C. S. Cassell, M. Hubble, M. A. P. Milling, and W. A. Dickson, “Baby walkers—still a major cause of infant burns,” Burns, vol. 23, no. 5, pp. 451–453, 1997.
- M. Crouchman, “The effects of babywalkers on early locomotor development,” Developmental Medicine and Child Neurology, vol. 28, no. 6, pp. 757–761, 1986.
- A. Siegel and R. Burton, “Effects of babywalkers on early locomotor development in human infants,” Developmental & Behavioral Pediatrics, vol. 20, pp. 355–361, 1999.
- I. B. Kauffman and M. Ridenour, “Influence of an infant walker on onset and quality of walking pattern of locomotion: an electromyographic investigation,” Perceptual and Motor Skills, vol. 45, no. 3, pp. 1323–1329, 1977.
- J. A. Andersen, An Introduction to Neural Network, MIT Press, Cambridge, Mass, USA, 1995.
- K. Madani and C. Sabourin, “Multi-level cognitive machine-learning based concept for human-like “artificial” walking: application to autonomous stroll of humanoid robots,” Neurocomputing, vol. 74, no. 8, pp. 1213–1228, 2011.
- H. Bülthoff, C. Wallraven, and M. Giese, “Perceptual robotic,” in Handbook of Robotics, B. Siciliano and O. Khatib, Eds., Springer, 2007.
- P. Zukow-Goldring and M. A. Arbib, “Affordances, effectivities, and assisted imitation: caregivers and the directing of attention,” Neurocomputing, vol. 70, no. 13–15, pp. 2181–2193, 2007.
- R. J. Brand, D. A. Baldwin, and L. A. Ashburn, “Evidence for “motionese”: modifications in mothers' infant-directed action,” Developmental Science, vol. 5, no. 1, pp. 72–83, 2002.
- R. Achanta, S. Hemami, E. Estrada, and S. Susstrunk, “Frequency-tuned salient region detection,” in Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition (CVPR '09), 2009.
- J. M. Wolfe and T. S. Horowitz, “What attributes guide the deployment of visual attention and how do they do it?” Nature Reviews Neuroscience, vol. 5, no. 6, pp. 495–501, 2004.
- T. W. Chen, Y. L. Chen, and S. Y. Chien, “Fast image segmentation based on K-means clustering with histograms in HSV color space,” in Proceedings of the IEEE 10th Workshop on Multimedia Signal Processing (MMSP '08), pp. 322–325, October 2008.
- X. Hou and L. Zhang, “Saliency detection: a spectral residual approach,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR'07), vol. 2, pp. 1–8, June 2007.
- R. Moreno, M. Graña, D. M. Ramik, and K. Madani, “Image segmentation by spherical coordinates,” in Proceedings of the 11th International Conference on Pattern Recognition and Information Processing (PRIP '11), pp. 112–115, 2011.
- J. H. Holland, Adaptation in Natural anti Artificial Systems: An introductory Analysis with Applications to Biology, Control and Artificial Intelligence, MIT Press, 1992.
- T. Liu, Z. Yuan, J. Sun et al., “Learning to detect a salient object,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 33, no. 2, pp. 353–367, 2011.
- D. M. Ramík, C. Sabourin, and K. Madani, “Hybrid salient object extraction approach with automatic estimation of visual attention scale,” in Proceedings of the 7th International Conference on Signal Image Technology & Internet-Based Systems (IEEE—SITIS '11), pp. 438–445, 2011.
- D. M. Ramík, C. Sabourin, and K. Madani, “A cognitive approach for robots’ vision using unsupervised learning and visual saliency,” in Advances in Computational Intelligence, vol. 6691 of LNCS, pp. 65–72, Springer, 2011.