Studies in Computational Intelligence

Growing Adaptive Machines

Combining Development and Learning in Artificial Neural Networks

Editors: Kowaliw, Taras, Bredeche, Nicolas, Doursat, René (Eds.)

  • Recent research in Growing Adaptive Machines
  • Presents development and learning in Artificial Neural Networks
  • Edited results of the DevLeaNN workshop on development and learning in Artificial Neural Networks held in Paris, October 27-28 2012
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  • Due: November 4, 2016
  • ISBN 978-3-662-50944-9
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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 Hyper NEAT 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.

Reviews

“This book considers the importance of biological plausibility in artificial neural networks (ANNs). … the book is recommended for those who want to know more about ANNs and their biologically inspired architectures, especially those related to learning.” (João Luís G. Rosa, Computing Reviews, March, 2015)


Table of contents (9 chapters)

  • Artificial Neurogenesis: An Introduction and Selective Review

    Kowaliw, Taras (et al.)

    Pages 1-60

  • A Brief Introduction to Probabilistic Machine Learning and Its Relation to Neuroscience

    Trappenberg, Thomas P.

    Pages 61-108

  • Evolving Culture Versus Local Minima

    Bengio, Yoshua

    Pages 109-138

  • Learning Sparse Features with an Auto-Associator

    Rebecchi, Sébastien (et al.)

    Pages 139-158

  • HyperNEAT: The First Five Years

    D’Ambrosio, David B. (et al.)

    Pages 159-185

Buy this book

eBook $99.00
price for USA (gross)
  • ISBN 978-3-642-55337-0
  • Digitally watermarked, DRM-free
  • Included format: EPUB, PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $129.00
price for USA
  • ISBN 978-3-642-55336-3
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
Softcover $129.00
price for USA
  • Customers within the U.S. and Canada please contact Customer Service at 1-800-777-4643, Latin America please contact us at +1-212-460-1500 (Weekdays 8:30am – 5:30pm ET) to place your order.
  • Due: November 4, 2016
  • ISBN 978-3-662-50944-9
  • Free shipping for individuals worldwide
Rent the ebook  
  • Rental duration: 1 or 6 month
  • low-cost access
  • online reader with highlighting and note-making option
  • can be used across all devices
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Bibliographic Information

Bibliographic Information
Book Title
Growing Adaptive Machines
Book Subtitle
Combining Development and Learning in Artificial Neural Networks
Editors
  • Taras Kowaliw
  • Nicolas Bredeche
  • René Doursat
Series Title
Studies in Computational Intelligence
Series Volume
557
Copyright
2014
Publisher
Springer-Verlag Berlin Heidelberg
Copyright Holder
Springer-Verlag Berlin Heidelberg
eBook ISBN
978-3-642-55337-0
DOI
10.1007/978-3-642-55337-0
Hardcover ISBN
978-3-642-55336-3
Softcover ISBN
978-3-662-50944-9
Series ISSN
1860-949X
Edition Number
1
Number of Pages
VII, 261
Number of Illustrations and Tables
68 b/w illustrations, 14 illustrations in colour
Topics