- Full Description
Using artificial intelligence and robustness as a unifying theme, this book comments on the fundamental strategies found in nature (such as redundancy, granularity, adaptation, repair, self-healing, etc.), and how an understanding of these can be used to further research into general design principles for artificial intelligence in the context of a diverse range of modern research areas and systems (including pervasive computing, autonomic computing, ambient intelligence, bioinformatics and others). Contributions from experts in the field provide an invaluable insight into a variety of cutting-edge research areas using artificial intelligence and robustness as the central theme. Practitioners in various fields will gain an insight into the important role robustness plays in natural and artificial systems in general, and in artificial intelligence in particular.
- Table of Contents
Table of Contents
- Part 1 Robustness in Computer Hardware, Software, Networks and Protocols.
- Robustness in Digital Hardware.
- 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 Dendrtic Cell Algorithm.
- 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 Principal 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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