Abstraction in Artificial Intelligence and Complex Systems

  • Lorenza Saitta
  • Jean-Daniel Zucker

Table of contents

  1. Front Matter
    Pages i-xvi
  2. Lorenza Saitta, Jean-Daniel Zucker
    Pages 1-9
  3. Lorenza Saitta, Jean-Daniel Zucker
    Pages 11-47
  4. Lorenza Saitta, Jean-Daniel Zucker
    Pages 49-63
  5. Lorenza Saitta, Jean-Daniel Zucker
    Pages 65-116
  6. Lorenza Saitta, Jean-Daniel Zucker
    Pages 117-139
  7. Lorenza Saitta, Jean-Daniel Zucker
    Pages 141-177
  8. Lorenza Saitta, Jean-Daniel Zucker
    Pages 179-222
  9. Lorenza Saitta, Jean-Daniel Zucker
    Pages 223-271
  10. Lorenza Saitta, Jean-Daniel Zucker
    Pages 273-327
  11. Lorenza Saitta, Jean-Daniel Zucker
    Pages 329-362
  12. Lorenza Saitta, Jean-Daniel Zucker
    Pages 363-387
  13. Lorenza Saitta, Jean-Daniel Zucker
    Pages 389-405
  14. Lorenza Saitta, Jean-Daniel Zucker
    Pages 407-411
  15. Back Matter
    Pages 413-484

About this book

Introduction

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.

Keywords

Abstraction Abstraction Applications Complex Systems Computational Complexity Knowledge Representation Machine Learning Representation Change

Authors and affiliations

  • Lorenza Saitta
    • 1
  • Jean-Daniel Zucker
    • 2
  1. 1., Dipartimento di Scienze e Innovazione TeUniversità Degli Studi Del Piemonte OrieAlessandriaItaly
  2. 2., International Research Unit UMMISCO 209Research Institute for Development (IRD)BondyFrance

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4614-7052-6
  • Copyright Information Springer Science+Business Media New York 2013
  • Publisher Name Springer, New York, NY
  • eBook Packages Computer Science
  • Print ISBN 978-1-4614-7051-9
  • Online ISBN 978-1-4614-7052-6
  • About this book