Abstract
In this paper the role of non-linear control structures for the development of multifunctional robot behavior in a self-organized way is discussed. This discussion is based on experiments where combinations of two behavioral tasks are incrementally evolved. The evolutionary experiments develop recurrent neural networks of general type in a systematically way. The resulting networks are investigated according to the underlying structure-function relations. These investigations point to necessary properties providing multifunctionality, scalability, and open-ended evolutionary strategies in Evolutionary Robotics.
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References
Beer, R.D.: An dynamical systems perspective on agent-environment interaction. Artificial Intelligence 72, 173–215 (1995)
Beer, R.D.: The dynamics of active categorical perception in an evolved model agent. Adaptive Behavior 11, 209–243 (2003)
Brooks, R.A.: Artificial life and real robots. In: Proceedings of the First European Conference on Artificial Life, pp. 3–10. MIT Press, Cambridge (1992)
Clark, A.: Being There: Putting Brain, Body and World Together Again. MIT Press, Cambridge (1997)
Bianco, R., Nolfi, S.: Toward open-ended evolutionary robotics: Evolving elemantary robotic units able to self-assemble and selfreproduce. Connection Science 16, 227–248 (2004)
Hülse, M., Wischmann, S., Pasemann, F.: Structure and function of evolved neuro-controllers for autonomous robots. Connection Science 16, 249–266 (2004)
Kelso, S.: Dynamic Patterns. MIT Press, Cambridge (1995)
Michel, O.: Khepera Simulator, Package version 2.0. Freeware mobile robot simulator written at the University of Nice Sophia-Antipolis by Oliver Michel, Downloadable from the World Wide Web at http://wwwi3s.unice.fr/~om/khep-sim.html
Mondada, F., Franzi, E., Ienne, P.: Mobile robots miniturization: A tool for investigation in control algorithms. In: Proc. of ISER 1993, Kyoto(1993)
Nolfi, S., Floreano, D.: Evolutionary Robotics: The Biology, Intelligence, and Technology of Self-Organizing Machines. MIT Press, Cambridge (2000)
Pasemann, F.: Characteristics of periodic attractors in neural ring networks. Neural Networks 8, 421–429 (1995)
Pasemann, F.: Structure and dynamics of recurrent neuro-modules. Theory in Biosciences 117, 1–17 (1998)
Pasemann, F., Steinmetz, U., Hülse, M., Lara, B.: Robot control and the evolution of modular neurodynamics. Theory in Biosciences 120, 311–326 (2001)
Walker, J., Garrett, S., Wilson, M.: Evolving controllers for real robots: A survey of the literature. Adaptive Behavior 11, 179–203 (2003)
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Hülse, M., Wischmann, S., Pasemann, F. (2005). The Role of Non-linearity for Evolved Multifunctional Robot Behavior. In: Moreno, J.M., Madrenas, J., Cosp, J. (eds) Evolvable Systems: From Biology to Hardware. ICES 2005. Lecture Notes in Computer Science, vol 3637. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11549703_11
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DOI: https://doi.org/10.1007/11549703_11
Publisher Name: Springer, Berlin, Heidelberg
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