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.
This special low-priced edition is for sale in India, Bangladesh, Bhutan, Maldives, Nepal, Myanmar, Pakistan and Sri Lanka only.
Contents: Part I Robustness in Computer Hardware, Software, Networks, and Protocols Robustness in Digital Hardware Multiagent-Based Fault Tolerance Management for Robustness A Two-Level Robustness Model for Self-Managing Software Systems Robustness in Network Protocols and Distributed Applications of the Internet Part II Robustness in Biology Inspired Systems Detecting Danger: The Dendritic Cell Algorithm Non-invasive Brain-Computer Interfaces for Semi-autonomous Assistive Devices Robust Learning of High-dimensional Biological Networks with Bayesian Networks Part III Robustness in Artificial Intelligence Systems Robustness in Nature as a Design Principle for Artificial Intelligence Feedback Structures as a Key Requirement for Robustness: Case Studies in Image Processing Exploiting Motor Modules in Modular Contexts in Humanoid Robotics Part IV Robustness in Space Applications Robustness as Key to Success for Space Missions Robust and Automated Space System Design Robust Bio-regenerative Life Support Systems Control
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