1.

Record Nr.

UNINA9910736985203321

Autore

Saitta Lorenza

Titolo

Abstraction in artificial intelligence and complex systems / / Lorenza Saitta, Jean-Daniel Zucker

Pubbl/distr/stampa

New York : , : Springer, , 2013

ISBN

1-4614-7052-8

Edizione

[1st ed. 2013.]

Descrizione fisica

1 online resource (xvi, 484 pages) : illustrations (chiefly color)

Collana

Gale eBooks

Disciplina

006.3

Soggetti

Artificial intelligence - Philosophy

Abstraction

Lingua di pubblicazione

Inglese

Formato

Materiale a stampa

Livello bibliografico

Monografia

Note generali

Description based upon print version of record.

Nota di bibliografia

Includes bibliographical references and index.

Nota di contenuto

Introduction -- 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.

Sommario/riassunto

Abstraction 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.