LEADER 04343nam  22007695  450 
001 9910299707403321
005 20200702140924.0
010   $a3-642-53734-0
024 7 $a10.1007/978-3-642-53734-9
035   $a(CKB)3710000000078892
035   $a(EBL)1636663
035   $a(OCoLC)871224054
035   $a(SSID)ssj0001089916
035   $a(PQKBManifestationID)11627803
035   $a(PQKBTitleCode)TC0001089916
035   $a(PQKBWorkID)11124888
035   $a(PQKB)10928043
035   $a(MiAaPQ)EBC1636663
035   $a(DE-He213)978-3-642-53734-9
035   $a(PPN)176118284
035   $a(EXLCZ)993710000000078892
100   $a20131219d2014      u|         0
101 0 $aeng
135   $aur|n|---|||||
181   $ctxt
182   $cc
183   $acr
200 10$aGuided Self-Organization: Inception /$fedited by Mikhail Prokopenko
205   $a1st ed. 2014.
210  1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d2014.
215   $a1 online resource (488 p.)
225 1 $aEmergence, Complexity and Computation,$x2194-7287 ;$v9
300   $aDescription based upon print version of record.
311   $a3-642-53733-2 
320   $aIncludes bibliographical references at the end of each chapters and index.
327   $aFoundational frameworks -- Coordinated behaviour and learning within an embodied agent -- Swarms and networks of agents.
330   $aIs it possible to guide the process of self-organisation towards specific patterns and outcomes?  Wouldn?t this be self-contradictory?   After all, a self-organising process assumes a transition into a more organised form, or towards a more structured functionality, in the absence of centralised control.  Then how can we place the guiding elements so that they do not override rich choices potentially discoverable by an uncontrolled process?  This book presents different approaches to resolving this paradox.  In doing so, the presented studies address a broad range of phenomena, ranging from autopoietic systems to morphological computation, and from small-world networks to information cascades in swarms.  A large variety of methods is employed, from spontaneous symmetry breaking to information dynamics to evolutionary algorithms, creating a rich spectrum reflecting this emerging field. Demonstrating several foundational theories and frameworks, as well as innovative practical implementations, Guided Self-Organisation: Inception, will be an invaluable tool for advanced students and researchers in a multiplicity of fields across computer science, physics and biology, including information theory, robotics, dynamical systems, graph theory, artificial life, multi-agent systems, theory of computation and machine learning.
410  0$aEmergence, Complexity and Computation,$x2194-7287 ;$v9
606   $aComputational complexity
606   $aComputers
606   $aArtificial intelligence
606   $aComputational intelligence
606   $aStatistical physics
606   $aComplexity$3https://scigraph.springernature.com/ontologies/product-market-codes/T11022
606   $aTheory of Computation$3https://scigraph.springernature.com/ontologies/product-market-codes/I16005
606   $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000
606   $aComputational Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/T11014
606   $aApplications of Nonlinear Dynamics and Chaos Theory$3https://scigraph.springernature.com/ontologies/product-market-codes/P33020
615  0$aComputational complexity.
615  0$aComputers.
615  0$aArtificial intelligence.
615  0$aComputational intelligence.
615  0$aStatistical physics.
615 14$aComplexity.
615 24$aTheory of Computation.
615 24$aArtificial Intelligence.
615 24$aComputational Intelligence.
615 24$aApplications of Nonlinear Dynamics and Chaos Theory.
676   $a536.7
702   $aProkopenko$b Mikhail$4edt$4http://id.loc.gov/vocabulary/relators/edt
801  0$bMiAaPQ
801  1$bMiAaPQ
801  2$bMiAaPQ
906   $aBOOK
912   $a9910299707403321
996   $aGuided Self-Organization: Inception$92169626
997   $aUNINA