Abstract
This paper introduces novel analyses that clarify why the dynamical systems approach is essential for studies of embodied cognition by revisiting author’s prior robot experiment studies. Firstly, we argue that the symbol grounding problems as well as the “situatedness” problems should be the consequences of lacking a shared metric space for the interactions between the higher cognitive levels based on symbol systems and the lower sensory-motor levels based on analog dynamical systems. In our prior studies it was proposed to employ recurrent neural networks (RNNs) as adaptive dynamical systems for implementing the top-down cognitive processes by which it is expected that dense interactions can be made between the cognitive and the sensory-motor levels. Our mobile robot experiments in prior works showed that the acquired internal models embedded in the RNN is naturally situated to the physical environment by means of entrainment between the RNN and the environmental dynamics. In the current study, further analysis was conducted on the dynamical structures obtained in the experiments, which turned out to clarify the essential differences between the conventional symbol systems and its equivalence realized in the adaptive dynamical systems.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Beer, R.: A dynamical systems perspective on agent-environment interaction. Artificial Intelligence 72, 173–215 (1995)
Crutchfield, J.: Inferring statistical complexity. Phys Rev Lett 63, 105–108 (1989)
Elman, J.: Finding structure in time. Cognitive Science 14, 179–211 (1990)
Gunji, Y., Konno, N.: Artificial Life with Autonomously Emerging Boundaries. App. Math. Computation 43, 271–298 (1991)
Harnad, S.: The symbol grounding problem. Physica D 42, 335–346 (1990)
Jordan, M., Rumelhart, D.: Forward models: supervised learning with a distal teacher. Cognitive Science 16, 307–354 (1992)
Kolen, J.: Exploring the computational capabilities of recurrent neural networks. PhD thesis, The Ohio State University (1994)
Kuipers, B.: A qualitative approach to robot exploration and map learning. In: AAAI Workshop Spatial Reasoning and Multi-Sensor Fusion, Chicago, pp. 774–779 (1987)
Mataric, M.: Integration of representation into goal-driven behavior-based robot. IEEE Trans. Robotics and Automation 8, 304–312 (1992)
Matsuno, K.: Physical Basis of Biology. CRC Press, Boca Raton (1989)
Maturana, H., Varela, F.: Autopoiesis and cognition: the realization of the living. D. Riedel Publishing, Boston (1980)
Pollack, J.: The induction of dynamical recognizers. Machine Learning 7, 227–252 (1991)
Rumelhart, D., Hinton, G., Williams, R.: Learning internal representations by error propagation. In: Rumelhart, D., Mclelland, J. (eds.) Parallel Distributed Processing. MIT Press, Cambridge (1986)
Tani, J.: Model-Based Learning for Mobile Robot Navigation from the Dynamical Systems Perspective. IEEE Trans. on SMC (B) 26, 421–436 (1996)
Tani, J.: An interpretation of the ”self” from the dynamical systems perspective: a constructivist approach. Journal of Consciousness Studies 5, 516–542 (1998)
Tani, J.: Learning to generate articulated behavior through the bottom-Up and the top-down interaction processes. Neural Networks 16, 11–23 (2003)
Tani, J., Fukumura, N.: Embedding a Grammatical Description in Deterministic Chaos: an Experiment in Recurrent Neural Learning. Biological Cybernetics 72, 365–370 (1995)
Tani, J., Ito, M.: Self-organization of behavior primitives as multiple attractor dynamics by the forwarding forward model network. Trans On IEEE SMC-B (2003) (in print)
Tani, J., Nolfi, S.: Learning to perceive the world as articulated: an approach for hierarchical learning in sensory-motor systems. Neural Networks 12, 1131–1141 (1999)
Tani, J., Yamamoto, J.: On the dynamics of robot exploration learning. Cognitive Systems Research 3, 459–470 (2002)
Tsuda, I.: Toward an interpretation of dynamic neural activity in terms of chaotic dynamical systems. Behavioral and Brain Sciences 24:5, 793–848 (2001)
Wiggins, S.: Introduction to Applied Nonlinear Dynamical Systems and Chaos. Springer, New York (1990)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Tani, J. (2003). Symbols and Dynamics in Embodied Cognition: Revisiting a Robot Experiment. In: Butz, M.V., Sigaud, O., Gérard, P. (eds) Anticipatory Behavior in Adaptive Learning Systems. Lecture Notes in Computer Science(), vol 2684. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45002-3_10
Download citation
DOI: https://doi.org/10.1007/978-3-540-45002-3_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-40429-3
Online ISBN: 978-3-540-45002-3
eBook Packages: Springer Book Archive