Exploiting the Power of Sensory-Motor Coordination
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One important implication of embodiment is that, by acting, agents partially determine the sensory patterns they receive from the environment. The motor actions performed by an agent, by modifying the agent’s position with respect to the external environment and/or the external environment itself, partially determine the type of sensory patterns received from the environment In this paper we investigate how agents can take advantage of this ability. In particular, we discuss how agents coordinate sensory and motor processes in order to (1) select sensory patterns which are not affected by the aliasing problem and avoid those which are; (2) select sensory patterns such that groups of patterns which require different responses do not strongly overlap; (3) exploit emergent behaviors that result from the interaction between the agent and the environment.
- Pfeifer, R. & Scheier, C. Sensory-motor coordination: The metaphor and beyond. Robotics and Autonomous Systems. 20 (1997) 157–178. CrossRef
- Nolfi, S. Parisi, D.: Learning to adapt to changing environments in evolving neural networks. Adaptive Behavior. 1: (1997) 99–105
- Whitehead, S.D. & Ballard, D. H.: Learning to perceive and act by trial and error. Machine Learning. 7 (1991) 45–83.
- Mondada, R., Franzi, E. & Ienne, P.: Mobile robot miniaturization: A tool for investigation in control algorithms: In: T. Yoshikawa & F. Miyazaki (eds.): Proceedings of the Third International Symposium on Experimental Robots, Kyoto, Japan (1993)
- Bajcsy, R.: Active Perception. Proceedings of the IEEE (76) 8: (1988) 996–1005 CrossRef
- Dill, M., Wolf, R., Heisenberg, M.: Visual pattern recognition in drosophila involves retinotopic matching. Nature. 355: (1993) 751–753. CrossRef
- Clark, A. & Thornton, C: Trading spaces: Computation, representation, and the limits of uniformed learning. Behavioral and Brain Sciences. 20 (1997) 57–90. CrossRef
- Elman, J.L.: Learning and development in neural networks: The importance of starting small. Cognition. 48 (1993) 71–99. CrossRef
- Scheier, C., Pfeifer, R. & Kunyioshi, Y. Embedded neural networks: exploiting constraints. Neural Networks. 11: (1998) 1551–1596. CrossRef
- Nolfi, S.: Adaptation as a more powerful tool than decomposition and integration. In: T. Fogarty and G. Venturini (Eds), Proceedings of the workshop on Evolutionary computing and Machine Learning, 13th International Conference on Machine Learning, Bari (1996)
- Nolfi, S.: Evolving non-trivial behavior on autonomous robots: Adaptation is more powerful than decomposition and integration. In T. Gomi (ed.): Evolutionary Robotics, Kanata, Canada: AAI Books (1997)
- Thornton C: Separability is a learner’s best friend. In: J.A. Bullinaria, D.W. Glasspool, & G. Houghton (eds.): Proceedings of the Neural Computation and Psychology Workshop: Connectionist Representations, London, Springer (1997).
- Exploiting the Power of Sensory-Motor Coordination
- Book Title
- Advances in Artificial Life
- Book Subtitle
- 5th European Conference, ECAL’99 Lausanne, Switzerland, September 13–17, 1999 Proceedings
- pp 173-182
- Print ISBN
- Online ISBN
- Series Title
- Lecture Notes in Computer Science
- Series Volume
- Series ISSN
- Springer Berlin Heidelberg
- Copyright Holder
- Springer-Verlag Berlin Heidelberg
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- Editor Affiliations
- 1. Laboratory of Microprocessors and Interfaces (LAMI) Department of Computer Science, Swiss Federal Institute of Technology (EPFL)
- Author Affiliations
- 4. Institute of Psychology, National Research Council (CNR), 15 Viale Marx, 00137, Rome, Italy
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