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010   $a1-4614-7052-8
024 7 $a10.1007/978-1-4614-7052-6
035   $a(OCoLC)847976995
035   $a(MiFhGG)GVRL6VJE
035   $a(EXLCZ)992560000000103586
100   $a20130301d2013      uy             0
101 0 $aeng
135   $aurun|---uuuua
181   $ctxt
182   $cc
183   $acr
200 10$aAbstraction in artificial intelligence and complex systems /$fLorenza Saitta, Jean-Daniel Zucker
205   $a1st ed. 2013.
210  1$aNew York :$cSpringer,$d2013.
215   $a1 online resource (xvi, 484 pages) $cillustrations (chiefly color)
225 0 $aGale eBooks
300   $aDescription based upon print version of record.
311   $a1-4899-8874-2 
311   $a1-4614-7051-X 
320   $aIncludes bibliographical references and index.
327   $aIntroduction -- Abstraction in Different Disciplines -- Abstraction in Artificial Intelligence -- Definitions of Abstraction -- Boundaries of Abstraction -- The KRA Model -- Abstraction Operators and Design Patterns -- Properties of the KRA Model -- Abstraction in Machine Learning -- Simplicity, Complex Systems, and Abstraction -- Case Studies and Applications -- Discussion -- Conclusion.
330   $aAbstraction is a fundamental mechanism underlying both human and artificial perception, representation of knowledge, reasoning and learning. This mechanism plays a crucial role in many disciplines, notably Computer Programming, Natural and Artificial Vision, Complex Systems, Artificial Intelligence and Machine Learning, Art, and Cognitive Sciences. This book first provides the reader with an overview of the notions of abstraction proposed in various disciplines by comparing both commonalities and differences.  After discussing the characterizing properties of abstraction, a formal model, the KRA model, is presented to capture them. This model makes the notion of abstraction easily applicable by means of the introduction of a set of abstraction operators and abstraction patterns, reusable across different domains and applications. It is the impact of abstraction in Artificial Intelligence, Complex Systems and Machine Learning which creates the core of the book.  A general framework, based on the KRA model, is presented, and its pragmatic power is illustrated with three case studies: Model-based diagnosis, Cartographic  Generalization, and learning Hierarchical Hidden Markov Models.
606   $aArtificial intelligence$xPhilosophy
606   $aAbstraction
615  0$aArtificial intelligence$xPhilosophy.
615  0$aAbstraction.
676   $a006.3
700   $aSaitta$b Lorenza$4aut$4http://id.loc.gov/vocabulary/relators/aut$01381744
702   $aZucker$b Jean-Daniel
801  0$bMiFhGG
801  1$bMiFhGG
906   $aBOOK
912   $a9910736985203321
996   $aAbstraction in Artificial Intelligence and Complex Systems$93424591
997   $aUNINA