Robust Intelligent Systems

Using artificial intelligence and robustness as a unifying theme, this book comments on the fundamental strategies found in nature and how an understanding of these can be used to further research into general design principles for artificial intelligence.

Our time recognizes robustness as an important, all-pervading feature in the world around us. Despite its omnipresence, robustness is not entirely understood, rather dif?cult to de?ne, and, despite its obvious value in many situations, rather dif?cult to achieve. One of the goals of this edited book is to report on the topic of robustness from a variety and diverse range of ?elds and perspectives. We are interested, for instance, in fundamental strategies nature applies to make systems robustand arguably intelligentand how these strategies may hold as general design principles in modern technology. A particular focus is on computer-based systems and appli- tions. This in mind, the book has four main sections: Part I has a look at robustness in terms of underlying technologies and infrastr- tures upon which many computer-based intelligent systems reside and inves- gates robustness on the hardware and software level, but also in larger environments such as the Internet and self-managing systems. The contributions in Part II target robustness in research areas that are inspired by biology, including brain-computer interfaces, biological networks, and biological immune systems, for example. Part III involves the exciting ?eld of arti?cial intelligence. The chapters here discuss the value of robustness as a general design principle for arti?cial intelligence, stressing its potential in areas such as humanoid robotics and image processing.

Reflects the increasing interest in the importance of robustness as a design principle for AI Draws on strategies and examples of problem solving from nature (such as redundancy, granularity, adaptation etc,) in the design of robust systems

Klappentext

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.

Inhalt
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.- 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.- 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.- 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.