Growing Adaptive Machines

Combining Development and Learning in Artificial Neural Networks

  • Taras Kowaliw
  • Nicolas Bredeche
  • René Doursat

Part of the Studies in Computational Intelligence book series (SCI, volume 557)

Table of contents

  1. Front Matter
    Pages i-vii
  2. Taras Kowaliw, Nicolas Bredeche, Sylvain Chevallier, René Doursat
    Pages 1-60
  3. Yoshua Bengio
    Pages 109-138
  4. Sébastien Rebecchi, Hélène Paugam-Moisy, Michèle Sebag
    Pages 139-158
  5. David B. D’Ambrosio, Jason Gauci, Kenneth O. Stanley
    Pages 159-185
  6. Jean-Baptiste Mouret, Paul Tonelli
    Pages 251-261

About this book


The pursuit of artificial intelligence has been a highly active domain of research for decades, yielding exciting scientific insights and productive new technologies. In terms of generating intelligence, however, this pursuit has yielded only limited success. This book explores the hypothesis that adaptive growth is a means of moving forward. By emulating the biological process of development, we can incorporate desirable characteristics of natural neural systems into engineered designs, and thus move closer towards the creation of brain-like systems. The particular focus is on how to design artificial neural networks for engineering tasks.

The book consists of contributions from 18 researchers, ranging from detailed reviews of recent domains by senior scientists, to exciting new contributions representing the state of the art in machine learning research. The book begins with broad overviews of artificial neurogenesis and bio-inspired machine learning, suitable both as an introduction to the domains and as a reference for experts. Several contributions provide perspectives and future hypotheses on recent highly successful trains of research, including deep learning, the HyperNEAT model of developmental neural network design, and a simulation of the visual cortex. Other contributions cover recent advances in the design of bio-inspired artificial neural networks, including the creation of machines for classification, the behavioural control of virtual agents, the desi

gn of virtual multi-component robots and morphologies, and the creation of flexible intelligence. Throughout, the contributors share their vast expertise on the means and benefits of creating brain-like machines.

This book is appropriate for advanced students and practitioners of artificial intelligence and machine learning.


Computational Intelligence Development in Artificial Neural Networks Growing Adaptive Machines Learning in Artificial Neural Networks

Editors and affiliations

  • Taras Kowaliw
    • 1
  • Nicolas Bredeche
    • 2
  • René Doursat
    • 3
  1. 1.CNRSInstitut des Systèmes Complexes - Paris Île-de-FranceParisFrance
  2. 2.Institute of Intelligent Systems and Robotics, CNRS UMR 7222, Université Pierre et Marie CurieParisFrance
  3. 3.School of Biomedical EngineeringDrexel UniversityPhiladelphiaUSA

Bibliographic information

  • DOI
  • Copyright Information Springer-Verlag Berlin Heidelberg 2014
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Engineering
  • Print ISBN 978-3-642-55336-3
  • Online ISBN 978-3-642-55337-0
  • Series Print ISSN 1860-949X
  • Series Online ISSN 1860-9503
  • Buy this book on publisher's site