000 04291nam a22004935i 4500
001 978-3-642-55337-0
003 DE-He213
005 20200421111849.0
007 cr nn 008mamaa
008 140604s2014 gw | s |||| 0|eng d
020 _a9783642553370
_9978-3-642-55337-0
024 7 _a10.1007/978-3-642-55337-0
_2doi
050 4 _aQ342
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
082 0 4 _a006.3
_223
245 1 0 _aGrowing Adaptive Machines
_h[electronic resource] :
_bCombining Development and Learning in Artificial Neural Networks /
_cedited by Taras Kowaliw, Nicolas Bredeche, Ren�e Doursat.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg :
_bImprint: Springer,
_c2014.
300 _aVII, 261 p. 82 illus., 14 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aStudies in Computational Intelligence,
_x1860-949X ;
_v557
505 0 _aArtificial neurogenesis: An introduction and selective review -- A Brief Introduction to Probabilistic Machine Learning and its Relation to Neuroscience -- Evolving culture versus local minima -- Learning sparse features with an auto-associator -- HyperNEAT: the first five years -- Using the GReaNs (Genetic Regulatory evolving artificial Networks) platform for signal processing, animat control, and artificial multicellular development -- Constructing complex systems via activity-driven unsupervised Hebbian self-organization -- Neuro-centric and holocentric approaches to the evolution of developmental neural networks -- Artificial evolution of plastic neural networks: A few key concepts.
520 _aThe 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.
650 0 _aEngineering.
650 0 _aArtificial intelligence.
650 0 _aComputational intelligence.
650 1 4 _aEngineering.
650 2 4 _aComputational Intelligence.
650 2 4 _aArtificial Intelligence (incl. Robotics).
700 1 _aKowaliw, Taras.
_eeditor.
700 1 _aBredeche, Nicolas.
_eeditor.
700 1 _aDoursat, Ren�e.
_eeditor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783642553363
830 0 _aStudies in Computational Intelligence,
_x1860-949X ;
_v557
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-642-55337-0
912 _aZDB-2-ENG
942 _cEBK
999 _c56005
_d56005