FRANK HESSE () (Max-Planck-Institute for Dynamics and Self-Organization, Bernstein Center for Computational Neuroscience Göttingen, Bunsenstrasse 10, 37073 Göttingen, Germany; Department for Nonlinear Dynamics, Georg-August-University Göttingen, Bunsenstrasse 10, 37073 Göttingen, Germany) RALF DER () (Max-Planck-Institute for Mathematics in the Sciences, Inselstrasse 22, 04103 Leipzig, Germany) J. MICHAEL HERRMANN () (University of Edinburgh, School of Informatics, 10 Crichton Street, Edinburgh, EH8 9AB, Scotland, UK)
Abstract
We study an adaptive controller that adjusts its internal parameters by self-organization of its interaction with the environment. We show that the parameter changes that occur in this low-level learning process can themselves provide a source of information to a higher-level context-sensitive learning mechanism. In this way, the context is interpreted in terms of the concurrent low-level learning mechanism. The dual learning architecture is studied in realistic simulations of a foraging robot and of a humanoid hand that manipulated an object. Both systems are driven by the same low-level scheme, but use the second-order information in different ways. While the low-level adaptation continues to follow a set of rigid learning rules, the second-order learning modulates the elementary behaviors and affects the distribution of the sensory inputs via the environment.
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