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010   $a3-030-25432-1
024 7 $a10.1007/978-3-030-25432-2
035   $a(CKB)4100000009160311
035   $a(DE-He213)978-3-030-25432-2
035   $a(MiAaPQ)EBC5940916
035   $z(PPN)258847743
035   $a(PPN)24376880X
035   $a(EXLCZ)994100000009160311
100   $a20190828d2020      u|             0
101 0 $aeng
135   $aurnn|008mamaa
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aIntegrating Soft Computing into Strategic Prospective Methods$b[electronic resource] $eTowards an Adaptive Learning Environment Supported by Futures Studies /$fby Raúl Trujillo-Cabezas, José Luis Verdegay
205   $a1st ed. 2020.
210  1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2020.
215   $a1 online resource (XXII, 230 p.) 
225 1 $aStudies in Fuzziness and Soft Computing,$x1434-9922 ;$v387
311   $a3-030-25431-3 
327   $aIntroduction -- Strategic Prospective: Definitions and Key Concepts -- Fuzzy Optimization and Reasoning Approaches -- Constructing Models -- Modeling and Simulation of the Future -- Experimental Applications: An Overview of New Ways -- Meta-Prospective Toolbox -- A Cloud Environment: A first demo.
330   $aThis book discusses how to build optimization tools able to generate better future studies. It aims at showing how these tools can be used to develop an adaptive learning environment that can be used for decision making in the presence of uncertainties. The book starts with existing fuzzy techniques and multicriteria decision making approaches and shows how to combine them in more effective tools to model future events and take therefore better decisions. The first part of the book is dedicated to the theories behind fuzzy optimization and fuzzy cognitive map, while the second part presents new approaches developed by the authors with their practical application to trend impact analysis, scenario planning and strategic formulation. The book is aimed at two groups of readers, interested in linking the future studies with artificial intelligence. The first group includes social scientists seeking for improved methods for strategic prospective. The second group includes computer scientists and engineers seeking for new applications and current developments of Soft Computing methods for forecasting in social science, but not limited to this.
410  0$aStudies in Fuzziness and Soft Computing,$x1434-9922 ;$v387
606   $aComputational intelligence
606   $aOperations research
606   $aManagement science
606   $aArtificial intelligence
606   $aDecision making
606   $aComputational Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/T11014
606   $aOperations Research, Management Science$3https://scigraph.springernature.com/ontologies/product-market-codes/M26024
606   $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000
606   $aOperations Research/Decision Theory$3https://scigraph.springernature.com/ontologies/product-market-codes/521000
615  0$aComputational intelligence.
615  0$aOperations research.
615  0$aManagement science.
615  0$aArtificial intelligence.
615  0$aDecision making.
615 14$aComputational Intelligence.
615 24$aOperations Research, Management Science.
615 24$aArtificial Intelligence.
615 24$aOperations Research/Decision Theory.
676   $a006.3
700   $aTrujillo-Cabezas$b Raúl$4aut$4http://id.loc.gov/vocabulary/relators/aut$01228277
702   $aVerdegay$b José Luis$4aut$4http://id.loc.gov/vocabulary/relators/aut
801  0$bMiAaPQ
801  1$bMiAaPQ
801  2$bMiAaPQ
906   $aBOOK
912   $a9910483103703321
996   $aIntegrating Soft Computing into Strategic Prospective Methods$92851502
997   $aUNINA