Overview
- Provides the core and underlying principles and analysis of the different concepts in the framework of Collective Intelligence for modeling and controlling distributed Multi-Agent Systems
- Discusses in detail the modified Probability Collectives approach proposed by the authors
- Emphasizes development of the fundamental results from basic concepts
- Numerous examples/problems are worked out in the text allowing the reader to gain further insight into the associated concepts
- Written for engineers, scientists and students in Optimization, Computational Intelligence or Artificial Intelligence and particularly involved in the Collective Intelligence field
- Includes supplementary material: sn.pub/extras
Part of the book series: Intelligent Systems Reference Library (ISRL, volume 86)
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Table of contents (7 chapters)
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Front Matter
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Back Matter
Keywords
About this book
This book provides an emerging computational intelligence tool in the framework of collective intelligence for modeling and controlling distributed multi-agent systems referred to as Probability Collectives. In the modified Probability Collectives methodology a number of constraint handling techniques are incorporated, which also reduces the computational complexity and improved the convergence and efficiency. Numerous examples and real world problems are used for illustration, which may also allow the reader to gain further insight into the associated concepts.
Reviews
“The book contains numerous overviews of the optimization literature, and each chapter has a comprehensive bibliography. The book will be of interest to both students who are interested in optimization and practitioners.” (J. P. E. Hodgson, Computing Reviews, June, 2015)
Authors and Affiliations
Bibliographic Information
Book Title: Probability Collectives
Book Subtitle: A Distributed Multi-agent System Approach for Optimization
Authors: Anand Jayant Kulkarni, Kang Tai, Ajith Abraham
Series Title: Intelligent Systems Reference Library
DOI: https://doi.org/10.1007/978-3-319-16000-9
Publisher: Springer Cham
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer International Publishing Switzerland 2015
Hardcover ISBN: 978-3-319-15999-7Published: 23 March 2015
Softcover ISBN: 978-3-319-36521-3Published: 06 October 2016
eBook ISBN: 978-3-319-16000-9Published: 25 February 2015
Series ISSN: 1868-4394
Series E-ISSN: 1868-4408
Edition Number: 1
Number of Pages: IX, 157
Number of Illustrations: 68 b/w illustrations
Topics: Computational Intelligence, Artificial Intelligence, Complex Systems, Statistical Physics and Dynamical Systems