Adaptivity and Learning : An Interdisciplinary Debate

Kuhn, R.; Menzel, R. (EDT); Menzel, W. (EDT); Ratsch, U. (EDT); Richter, M. M. (EDT); Stamatescu, I. O. (EDT); Kuhn, R. (EDT); Menzel, R.; Menzel, W.; Ratsch, U.; Richter, M. M.

ISBN 10: 3540000917 / ISBN 13: 9783540000914

Published by Springer, 2003

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Bibliographic Details

• Title: Adaptivity and Learning : An Interdisciplinary Debate
• Publisher: Springer
• Publication Date: 2003
• Language: English
• ISBN 10: 3540000917
• ISBN 13: 9783540000914
• Binding: Hardcover
• Condition: As New

About this title

Synopsis

Adaptivity and learning have in recent decades become a common concern of scientific disciplines. These issues have arisen in mathematics, physics, biology, informatics, economics, and other fields more or less simultaneously. The aim of this publication is the interdisciplinary discourse on the phenomenon of learning and adaptivity. Different perspectives are presented and compared to find fruitful concepts for the disciplines involved. The authors select problems showing representative traits concerning the frame up, the methods and the achievements rather than to present extended overviews.

From the Back Cover

 Adaptivity and learning have in recent decades become a common concern of scientific disciplines. These issues have arisen in mathematics, physics, biology, informatics, economics, and other fields more or less simultaneously. The aim of this publication is the interdisciplinary discourse on the phenomenon of learning and adaptivity. Different perspectives are presented and compared to find fruitful concepts for the disciplines involved. The authors select problems showing representative traits concerning the frame up, the methods and the achievements rather than to present extended overviews.

To foster interdisciplinary dialogue, this book presents diverse perspectives from various scientific fields, including:

- The biological perspective: e.g., physiology, behaviour;

- The mathematical perspective: e.g., algorithmic and stochastic learning;

- The physics perspective: e.g., learning for artificial neural networks;

- The "learning by experience" perspective: reinforcement learning, social learning, artificial life;

- The cognitive perspective: e.g., deductive/inductive procedures, learning and language learning as a high level cognitive process;

- The application perspective: e.g., robotics, control, knowledge engineering.