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 design 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.

Generally, spontaneous pattern formation phenomena are random and repetitive, whereas elaborate devices are the deterministic product of human design.
Yet, biological organisms and collective insect constructions are exceptional examples of complex systems that are both self-organized and architectural.
This book is the first initiative of its kind toward establishing a new field of research, Morphogenetic Engineering, to explore the modeling and implementation of self-architecturing systems. Particular emphasis is placed on the programmability and computational abilities of self-organization, properties that are often underappreciated in complex systems science while, conversely, the benefits of self-organization are often underappreciated in engineering methodologies.
Altogether, the aim of this work is to provide a framework for and examples of a larger class of self-architecturing systems, while addressing fundamental questions such as
> How do biological organisms carry out morphogenetic tasks so reliably?
> Can we extrapolate their self-formation capabilities to engineered systems?
> Can physical systems be endowed with information (or informational systems be embedded in physics) so as to create autonomous morphologies and functions?
> What are the core principles and best practices for the design and engineering of such morphogenetic systems?
The intended audience consists of researchers and graduate students who are working on, starting to work on, or interested in programmable self-organizing systems in a wide range of scientific fields, including computer science, robotics, bioengineering, control engineering, physics, theoretical biology, mathematics, and many others.

Generally, spontaneous pattern formation phenomena are random and repetitive, whereas elaborate devices are the deterministic product of human design. Yet, biological organisms and collective insect constructions are exceptional examples of complex systems that are both self-organized and architectural. This book is the first initiative of its kind toward establishing a new field of research, Morphogenetic Engineering, to explore the modeling and implementation of "self-architecturing" systems. Particular emphasis is placed on the programmability and computational abilities of self-organization, properties that are often underappreciated in complex systems science-while, conversely, the benefits of self-organization are often underappreciated in engineering methodologies. Altogether, the aim of this work is to provide a framework for and examples of a larger class of "self-architecturing" systems, while addressing fundamental questions such as > How do biological organisms carry out morphogenetic tasks so reliably? > Can we extrapolate their self-formation capabilities to engineered systems? > Can physical systems be endowed with information (or informational systems be embedded in physics) so as to create autonomous morphologies and functions? > What are the core principles and best practices for the design and engineering of such morphogenetic systems? The intended audience consists of researchers and graduate students who are working on, starting to work on, or interested in programmable self-organizing systems in a wide range of scientific fields, including computer science, robotics, bioengineering, control engineering, physics, theoretical biology, mathematics, and many others.

"Mitigating Paradox at the eSociety Tipping Point" In the first two decades of the past Century, having as driving factor the automobile and its mass production, the command economy has radically changed our lifestyles, enabling the creation of offices, suburbs, fast food restaurants and unified school d- tricts. With the Internet as driving factor, socio-technical and industrial eNetworked ecosystems are about to change our lives again in these two decades of the twenty-first century, and we are just approaching the tipping point. As we have just reached the point where the tremendous changes fueled by concerted efforts in information communication technologies (ICT) research are unraveling the old society this is creating a lot of d- comfort, confusion and sometimes opposition from the traditional mainstream. This disconnect is being deepened even more by the rocketing speed of technological ICT advances. As technology is getting ahead of society, the old ways, although still do- nant, become more and more dysfunctional and we are experiencing an "age of pa- dox" as the new ways disrupt the way we used to do things and even the way we used to think about the world. Just like the major inventions that shaped the last century were made by 1920, it is expected that the major inventions that will shape the twen- first century are going to be made by 2020.