Developing social action capabilities in a humanoid robot using an interaction history architecture

2008 8th IEEE-RAS International Conference on Humanoid Robots, Humanoids 2008

Volumn , Issue , 2008, Pages 609-616

  Mirza, Naeem Assif ^a,b   Nehaniv, Chrystopher L ^a   Dautenhahn, Kerstin ^a   Te Boekhorst, René ^a  

^a UNIVERSITY OF HERTFORDSHIRE   (United Kingdom)

^b ISTITUTO ITALIANO DI TECNOLOGIA   (Italy)

Author keywords

[No Author keywords available]

Indexed keywords

GAME THEORY; INTELLIGENT ROBOTS; REINFORCEMENT; VISUAL COMMUNICATION;

FACIAL EXPRESSIONS; HUMANOID ROBOTS; INTERACTION HISTORY; VISUAL FEEDBACKS;

ROBOTICS;

EID: 63549108510     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICHR.2008.4756013     Document Type: Conference Paper

References (22)

1. N. A. Mirza, C. L. Nehaniv, K. Dautenhahn, and R. te Boekhorst, "Grounded sensorimotor interaction histories in an information theoretic metric space for robot ontogeny," Adaptive Behaviour, vol. 15, no. 2, pp. 167-187, 2007.
2. M. L. Anderson, "Embodied cognition: A field guide," Artificial Intelligence, vol. 149, pp. 91-130, 2003.
3. G. Lakoff and M. Johnson, Philosophy in the Flesh: The Embodied Mind and Its Challenge to Western Thought. Basic Books, New York, 1999.
4. A. M. Glenberg, "What is memory for?" Behavioral and Brain Sciences, vol. 20, no. 1, March 1997.
5. C. L. Nehaniv, D. Polani, K. Dautenhahn, R. te Boekhorst, and L. Cañamero, "Meaningful information, sensor evolution, and the temporal horizon of embodied organisms," in Artificial Life VIII. MIT Press, 2002, pp. 345-349.
6. N. A. Mirza, C. L. Nehaniv, K. Dautenhahn, and R. te Boekhorst, "Anticipating future experience using grounded sensorimotor informational relationships," in Artificial Life XI 11th International Conference on the Simulation and Synthesis of Living Systems. Winchester, UK: University of Southampton, August 2008, in press.
7. L. Vygotsky, Mind and society: The development of higher mental processes. Cambridge, MA: Harvard University Press., 1978.
8. J. Crutchfield, "Information and its metric," in Nonlinear Structures in Physical Systems - Pattern Formation, Chaos and Waves, L. Lam and H. Morris, Eds. New York: Springer-Verlag, 1990, pp. 119-130.
9. A. Meltzoff and M. Moore, "Explaining facial imitation: a theoretical model," Early Development and Parenting, vol. 6, pp. 179-192, 1997.
10. C. L. Nehaniv, "Sensorimotor experience and its metrics," in Proc. 2005 IEEE Congress on Evolutionary Computation, vol. 1. Edinburgh, Scotland: IEEE Press, 2-5 Sept. 2005, pp. 142-149.
11. P. Rochat, J. G. Querido, and T. Striano, "Emerging sensitivity to the timing and structure of protoconversation in early infancy," Developmental Psychology, vol. 35, no. 4, pp. 950-957, 1999.
12. OpenCV, "Open computer vision library (gpl licence)," http://sourceforge.net/projects/opencvlibrary/, 2000.
13. G. Metta, P. Fitzpatrick, and L. Natale, "YARP: Yet Another Robot Platform," International Journal of Advanced Robotic Systems, vol. 3, no. 1, pp. 43-48, 2006.
14. J. Kolodner, Case-based Reasoning. Morgan Kaufman, 1993.
15. A. Ram and J. C. Santamaria, "Continuous case-based reasoning," Artificial Intelligence, vol. 90, no. 1-2, pp. 25-77, 1997.
16. R. S. Sutton and A. G. Barto, Reinforcement Learning: An Introduction. MIT Press, March 1998.
17. A. G. Barto and Ö. Şimşek, "Intrinsic motivation for reinforcement learning systems," in Proceedings of the Thirteenth Yale Workshop on Adaptive and Learning Systems, 2005, pp. 113-118.
18. A. Bonarini, A. Lazaric, M. Restelli, and P. Vitali, "Self- development framework for reinforcement learning agents," in Proceedings the of 5th International Conference on Development and Learning (ICDL 2006), 2006.
19. L.-J. Lin and T. Mitchell, "Reinforcement learning with hidden states," in From animals to animats 2: Proceedings of the second international conference on simulation of adaptive behavior, J.-A. Meyer, H. L. Roitblat, and S. W. Wilson, Eds. MIT Press, Cambridge, MA, 1992, pp. 271-280.
20. B. Bakker, "Reinforcement learning with long short-term memory," in Advances in Neural Information Processing Systems 14, T. G. Dietterich, S. Becker, and Z. Ghahramani, Eds. MIT Press, Cambridge, MA, 2002.
21. R. McCallum, "Hidden state and reinforcement learning with instancebased state identification," Systems, Man, and Cybernetics, Part B, IEEE Transactions on, vol. 26, no. 3, pp. 464-473, 1996.
22. M. Ogino, T. Ooide, A. Watanabe, and M. Asada, "Acquiring peekaboo communication: Early communication model based on reward prediction," in Proceedings of the 6th IEEE International Conference on Developoment and Learning. IEEE CDROM, 2007.