Overview
- 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
- Includes supplementary material: sn.pub/extras
Part of the book series: Studies in Computational Intelligence (SCI, volume 557)
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Table of contents (9 chapters)
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Front Matter
Keywords
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)
Editors and Affiliations
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
DOI: https://doi.org/10.1007/978-3-642-55337-0
Publisher: Springer Berlin, Heidelberg
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer-Verlag Berlin Heidelberg 2014
Hardcover ISBN: 978-3-642-55336-3Published: 26 June 2014
Softcover ISBN: 978-3-662-50944-9Published: 17 September 2016
eBook ISBN: 978-3-642-55337-0Published: 04 June 2014
Series ISSN: 1860-949X
Series E-ISSN: 1860-9503
Edition Number: 1
Number of Pages: VII, 261
Number of Illustrations: 68 b/w illustrations, 14 illustrations in colour