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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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
Print ISBN: 978-3-540-28736-0
Online ISBN: 978-3-540-28737-7
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