1 option
Abstraction in Artificial Intelligence and Complex Systems / by Lorenza Saitta, Jean-Daniel Zucker.
- Format:
- Book
- Author/Creator:
- Saitta, Lorenza, 1944- author.
- Zucker, Jean-Daniel, author.
- Series:
- Computer Science (Springer-11645)
- Language:
- English
- Subjects (All):
- Artificial intelligence.
- Optical data processing.
- Data mining.
- Application software.
- Artificial Intelligence.
- Image Processing and Computer Vision.
- Data Mining and Knowledge Discovery.
- Computer Appl. in Arts and Humanities.
- Local Subjects:
- Artificial Intelligence.
- Image Processing and Computer Vision.
- Data Mining and Knowledge Discovery.
- Computer Appl. in Arts and Humanities.
- Physical Description:
- 1 online resource (XVI, 484 pages)
- Edition:
- First edition 2013.
- Contained In:
- Springer eBooks
- Place of Publication:
- New York, NY : Springer New York : Imprint: Springer, 2013.
- System Details:
- text file PDF
- Summary:
- 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.
- Contents:
- 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.
- Other Format:
- Printed edition:
- ISBN:
- 978-1-4614-7052-6
- 9781461470526
- Access Restriction:
- Restricted for use by site license.
The Penn Libraries is committed to describing library materials using current, accurate, and responsible language. If you discover outdated or inaccurate language, please fill out this feedback form to report it and suggest alternative language.