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Robust Intelligent Systems

โœ Scribed by Roger Woods, Gaye Lightbody (auth.), Alfons Schuster (eds.)


Publisher
Springer-Verlag London
Year
2008
Tongue
English
Leaves
297
Edition
1
Category
Library

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โœฆ Synopsis


Robustness is an intriguing phenomenon in many complex intelligent systems, natural and artificial alike. This book investigates the relevance of robustness in a modern intelligent computing context, where many systems take inspiration from fundamental problem-solving strategies found in nature such as redundancy, granularity, adaptation, repair, and self-healing for creating robust systems. The book explores the value these strategies may have as general design principles in a diverse range of areas including the computer technology underlying many intelligent systems, and also systems and applications inspired by biology, artificial intelligence, and intelligent space exploration. The topics covered include computer hardware and software, networks and protocols, brain-computer interfaces, biological networks and immune systems, humanoid robotics, image processing, artificial neural networks, genetic algorithms, chaos theory, and other soft computing techniques, as well as space system design and bio-regenerative life support systems.

As modern information technology and modern computing are integral to many areas of human life and are used in increasingly more sophisticated and challenging ways, by looking at the relevance and importance of robustness as found in nature as a design principle for intelligent systems, this book provides a unique resource for practitioners in a wide variety of fields.

โœฆ Table of Contents


Front Matter....Pages I-XI
Front Matter....Pages 1-1
Robustness in Digital Hardware....Pages 3-21
Multiagent-Based Fault Tolerance Management for Robustness....Pages 23-41
A Two-Level Robustness Model for Self-Managing Software Systems....Pages 43-60
Robustness in Network Protocols and Distributed Applications of the Internet....Pages 61-86
Front Matter....Pages 87-87
Detecting Danger: The Dendritic Cell Algorithm....Pages 89-112
Non-invasive Brain-Computer Interfaces for Semi-autonomous Assistive Devices....Pages 113-138
Robust Learning of High-dimensional Biological Networks with Bayesian Networks....Pages 139-161
Front Matter....Pages 163-163
Robustness in Nature as a Design Principle for Artificial Intelligence....Pages 164-186
Feedback Structures as a Key Requirement for Robustness: Case Studies in Image Processing....Pages 189-208
Exploiting Motor Modules in Modular Contexts in Humanoid Robotics....Pages 209-229
Front Matter....Pages 231-231
Robustness as Key to Success for Space Missions....Pages 232-248
Robust and Automated Space System Design....Pages 251-271
Robust Bio-regenerative Life Support Systems Control....Pages 273-295
Back Matter....Pages 297-299

โœฆ Subjects


Artificial Intelligence (incl. Robotics); Theory of Computation


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