Adaptivity and Learning

An Interdisciplinary Debate

  • Reimer Kühn
  • Randolf Menzel
  • Wolfram Menzel
  • Ulrich Ratsch
  • Michael M. Richter
  • Ion-Olimpiu Stamatescu

Table of contents

  1. Front Matter
    Pages I-XII
  2. Adaptivity and Learning — an Interdisciplinary Debate

    1. Reimer Kühn, Randolf Menzel, Wolfram Menzel, Ulrich Ratsch, Michael M. Richter, Ion-Olimpiu Stamatescu
      Pages 1-4
  3. Biology and Behaviour of Adaptation and Learning

  4. Physics Approach to Learning — Neural Networks and Statistics

  5. Mathematical Models of Learning

    1. Front Matter
      Pages 109-109
    2. Wolfam Menzel
      Pages 111-113
    3. Ferdinando Cicalese, Daniele Mundici
      Pages 115-140
    4. Kerstin Dautenhahn, Chrystopher L. Nehaniv, Aris Alissandrakis
      Pages 141-159
    5. Jeremy Wyatt
      Pages 161-186
    6. Ion-Olimpiu Stamatescu
      Pages 187-209
  6. Learning by Experience

    1. Front Matter
      Pages 211-211
    2. Gunther Heidemann, Helge Ritter
      Pages 213-215
    3. Michael M. Richter
      Pages 243-264
  7. Human-Like Cognition and AI Learning

    1. Front Matter
      Pages 281-281
    2. Michael M. Richter
      Pages 283-284
    3. Gunther Heidemann, Helge Ritter
      Pages 285-309
    4. Martin Riedmiller, Artur Merke
      Pages 311-328
    5. Michael M. Richter
      Pages 329-345

About this book


 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.


Adaptation Mathematica artificial intelligence cognition complexity information processing issue learning machine learning mathematics networks neural network neural networks robot statistics

Editors and affiliations

  • Reimer Kühn
    • 1
  • Randolf Menzel
    • 2
  • Wolfram Menzel
    • 3
  • Ulrich Ratsch
    • 4
  • Michael M. Richter
    • 5
  • Ion-Olimpiu Stamatescu
    • 4
    • 1
  1. 1.Institut für Theoretische PhysikUniversität HeidelbergHeidelbergGermany
  2. 2.NeurobiologieFreie Universität BerlinBerlinGermany
  3. 3.Institut für Logik, Komplexität und DeduktionssystemeUniversität KarlsruheKarlsruheGermany
  4. 4.FEStHeidelbergGermany
  5. 5.FB InformatikUniversity of KaiserslauternKaiserslauternGermany

Bibliographic information

  • DOI
  • Copyright Information Springer-Verlag Berlin Heidelberg 2003
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Springer Book Archive
  • Print ISBN 978-3-642-05510-2
  • Online ISBN 978-3-662-05594-6
  • Buy this book on publisher's site