MODULATED EXPLORATORY DYNAMICS CAN SHAPE SELF-ORGANIZED BEHAVIOR

Frank Hesse (), Ralf der () and J. Michael Herrmann ()
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Frank Hesse: Max-Planck-Institute for Dynamics and Self-Organization, Bernstein Center for Computational Neuroscience 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

Advances in Complex Systems (ACS), 2009, vol. 12, issue 03, 273-291

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.

Keywords: Self-organization; homeokinesis; Hebbian learning (search for similar items in EconPapers)
Date: 2009
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DOI: 10.1142/S0219525909002258

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