Quantifying patterns of agent-environment interaction

Robotics and Autonomous Systems

Volumn 54, Issue 2, 2006, Pages 150-158

  Tarapore, Danesh ^a   Lungarella, Max ^b   Gómez, Gabriel ^c  

^a EPFL   (Switzerland)

^b UNIVERSITY OF TOKYO   (Japan)

^c UNIVERSITY OF ZURICH   (Switzerland)

Author keywords

Fingerprinting agent environment interaction; Self structuring of information; Sensory motor coordination

Indexed keywords

FINGERPRINTING AGENT-ENVIRONMENT INTERACTION; SELF-STRUCTURING OF INFORMATION; SENSORY-MOTOR COORDINATION; SENSORY-MOTOR INTERACTIONS;

CORRELATION DETECTORS; ROBOTIC ARMS; SENSORS; STATISTICAL MECHANICS;

ROBOTS;

EID: 30944458411     PISSN: 09218890     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.robot.2005.09.024     Document Type: Conference Paper

References (17)

1. S.J. Lederman, and R.L. Klatzky Haptic exploration and object representation M. Goodale Vision and Action: The Control of Grasping 1990 Ablex New Jersey 98 109
2. A.L. Yarbus Eye Movements and Vision 1967 Plenum Press
3. T.S. Lee, and S.X. Yu An information-theoretic framework for understanding saccadic behaviors Proc. of 1st Int. Conf. on Neural Information Processing 1999 MIT Press Cambridge, MA
4. E. Thelen, and L. Smith A Dynamic Systems Approach to the Development of Cognition and Action 1994 MIT Press Cambridge, MA (A Bradford Book)
5. D. Ballard Animate vision Artificial Intelligence 48 1 1991 57 86
6. E.J. Gibson, and A.D. Pick An Ecological Approach to Perceptual Learning and Development 2000 Oxford University Press New York, NY
7. M. Lungarella, R. Pfeifer, Robots as cognitive tools: information-theoretic analysis of sensory-motor data, in: Proc. of 2nd Int. IEEE/RSJ Conf. on Humanoid Robotics, 2001, pp. 245-252
8. M. Lungarella, T. Pegors, D. Bulwinkle, and O. Sporns Methods for quantifying the informational structure of sensory and motor data Neuroinformatics 3 3 2005 243 262
9. S. Nolfi Power and limit of reactive agents Neurocomputing 49 2002 119 145
10. O. Sporns, and T. Pegors Information-theoretic aspects of embodied artificial intelligence F. Iida R. Pfeifer L. Steels Y. Kuniyoshi Embodied Artificial Intelligence 2004 Springer-Verlag Heidelberg 74 85
11. R. Pfeifer, and C. Scheier Sensory-motor coordination: the metaphor and beyond Robotics and Autonomous Systems 20 1997 157 178
12. C. Scheier, R. Pfeifer, Information theoretic implications of embodiment for neural network learning, in: Int. Conf. on Artificial Neural Networks, 1997, pp. 691-696
13. R. Te Boekhorst, M. Lungarella, R. Pfeifer, Dimensionality reduction through sensory-motor coordination, in: O. Kaynak, E. Alpaydin, E. Oja, L. Xu (Eds.), Proc. of the Joint Int. Conf. in Artificial Neural Networks and Neural Information Processing, 2003, pp. 496-503
14. R. Pfeifer, and C. Scheier Understanding Intelligence 1999 MIT Press Cambridge, MA
15. T. Cover, and J. Thomas Elements of Information Theory 1991 John Wiley New York, NY
16. R. Steuer, J. Kurths, C. Daub, J. Weise, and J. Selbig The mutual information: detecting and evaluating dependencies between variables Bioinformatics 18 Suppl. 2 2002 231 240
17. M. Roulston Estimating the errors on measured entropy and mutual information Physica D 125 1999 285 294