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Advances in Applied Self-organizing Systems
The main challenge faced by designers of self-organizing systems is how to validate and control non-deterministic dynamics. Over-engineering the system may completely suppress self-organization with an outside influence, eliminating emergent patterns and decreasing robustness, adaptability and scala...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor Corporativo: | |
| Otros Autores: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
London :
Springer London : Imprint: Springer,
2008.
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| Edición: | 1st ed. 2008. |
| Colección: | Advanced Information and Knowledge Processing,
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| Temas: | |
| Acceso en línea: | Texto Completo |
MARC
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| 245 | 1 | 0 | |a Advances in Applied Self-organizing Systems |h [electronic resource] / |c edited by Mikhail Prokopenko. |
| 250 | |a 1st ed. 2008. | ||
| 264 | 1 | |a London : |b Springer London : |b Imprint: Springer, |c 2008. | |
| 300 | |a XII, 376 p. 144 illus. |b online resource. | ||
| 336 | |a text |b txt |2 rdacontent | ||
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| 338 | |a online resource |b cr |2 rdacarrier | ||
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| 490 | 1 | |a Advanced Information and Knowledge Processing, |x 2197-8441 | |
| 505 | 0 | |a Design vs. Self-organization -- Foundations and Formalizations of Self-organization -- Distributed Management and Control -- Self-Organizing Traffic Lights: A Realistic Simulation -- A Self-organizing Sensing System for Structural Health Monitoring of Aerospace Vehicles -- Decentralized Decision Making for Multiagent Systems -- Learning Mutation Strategies for Evolution and Adaptation of a Simulated Snakebot -- Self-Organization as Phase Transition in Decentralized Groups of Robots: A Study Based on Boltzmann Entropy -- Distributed Control of Microscopic Robots in Biomedical Applications -- Self-Organizing Computation -- Self-Organizing Digital Systems -- Self-organizing Nomadic Services in Grids -- Immune System Support for Scheduling -- Formal Immune Networks: Self-Organization and Real-World Applications -- A Model for Self-Organizing Data Visualization Using Decentralized Multiagent Systems -- Emergence of Traveling Localizations in Mutualistic-Excitation Media -- Discussion -- A Turing Test for Emergence. | |
| 520 | |a The main challenge faced by designers of self-organizing systems is how to validate and control non-deterministic dynamics. Over-engineering the system may completely suppress self-organization with an outside influence, eliminating emergent patterns and decreasing robustness, adaptability and scalability. Whilst leaving too much non-determinism in the system's behaviour may make its verification and validation almost impossible. This book presents the state-of-the-practice in successfully engineered self-organizing systems, and examines ways to balance design and self organization in the context of applications. As demonstrated throughout, finding this balance helps to deal with diverse practical challenges. The book begins with the more established fields of traffic management and structural health monitoring, building up towards robotic teams, solving challenging tasks deployed in tough environments. The second half of the book follows with a deeper look into the micro-level, and considers local interactions between agents. These interactions lead towards self-modifying digital circuitry and self-managing grids, self-organizing data visualization and intrusion detection in computer networks, immunocomputing and nature-inspired computation, and eventually to artificial life. The case studies described illustrate the richness of the topic and provide guidance to its intricate areas. Many algorithms proposed and discussed in this volume are biologically inspired and readers will also gain an insight into cellular automata, genetic algorithms, artificial immune systems, snake-like locomotion, ant foraging, birds flocking and mutualistic biological ecosystems, amongst others. Demonstrating the practical relevance and applicability of self-organization, this book will be of interest to advanced students and researchers in a wide range of fields. | ||
| 650 | 0 | |a Artificial intelligence. | |
| 650 | 0 | |a System theory. | |
| 650 | 0 | |a Control theory. | |
| 650 | 0 | |a Social sciences. | |
| 650 | 0 | |a Humanities. | |
| 650 | 0 | |a Pattern recognition systems. | |
| 650 | 0 | |a Computer simulation. | |
| 650 | 0 | |a Computer science. | |
| 650 | 1 | 4 | |a Artificial Intelligence. |
| 650 | 2 | 4 | |a Systems Theory, Control . |
| 650 | 2 | 4 | |a Humanities and Social Sciences. |
| 650 | 2 | 4 | |a Automated Pattern Recognition. |
| 650 | 2 | 4 | |a Computer Modelling. |
| 650 | 2 | 4 | |a Theory of Computation. |
| 700 | 1 | |a Prokopenko, Mikhail. |e editor. |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
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| 776 | 0 | 8 | |i Printed edition: |z 9781848006683 |
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| 776 | 0 | 8 | |i Printed edition: |z 9781846289811 |
| 830 | 0 | |a Advanced Information and Knowledge Processing, |x 2197-8441 | |
| 856 | 4 | 0 | |u https://doi.uam.elogim.com/10.1007/978-1-84628-982-8 |z Texto Completo |
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| 950 | |a Computer Science (SpringerNature-11645) | ||
| 950 | |a Computer Science (R0) (SpringerNature-43710) | ||