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E-raamat: Organic Computing - Technical Systems for Survival in the Real World

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  • Formaat: EPUB+DRM
  • Sari: Autonomic Systems
  • Ilmumisaeg: 28-Dec-2017
  • Kirjastus: Birkhauser Verlag AG
  • Keel: eng
  • ISBN-13: 9783319684772
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Organic Computing - Technical Systems for Survival in the Real WorldSuurem pilt
  • Formaat: EPUB+DRM
  • Sari: Autonomic Systems
  • Ilmumisaeg: 28-Dec-2017
  • Kirjastus: Birkhauser Verlag AG
  • Keel: eng
  • ISBN-13: 9783319684772
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This book is a comprehensive introduction into Organic Computing (OC), presenting systematically the current state-of-the-art in OC. It starts with motivating examples of self-organising, self-adaptive and emergent systems, derives their common characteristics and explains the fundamental ideas for a formal characterisation of such systems. Special emphasis is given to a quantitative treatment of concepts like self-organisation, emergence, autonomy, robustness, and adaptivity. The book shows practical examples of architectures for OC systems and their applications in traffic control, grid computing, sensor networks, robotics, and smart camera systems. The extension of single OC systems into collective systems consisting of social agents based on concepts like trust and reputation is explained. OC makes heavy use of learning and optimisation technologies; a compact overview of these technologies and related approaches to self-organising systems is provided.





So far, OC literature has been published with the researcher in mind. Although the existing books have tried to follow a didactical concept, they remain basically collections of scientific papers. A comprehensive and systematic account of the OC ideas, methods, and achievements in the form of a textbook which lends itself to the newcomer in this field has been missing so far. The targeted reader of this book is the master student in Computer Science, Computer Engineering or Electrical Engineering - or any other newcomer to the field of Organic Computing with some technical or Computer Science background. Readers can seek access to OC ideas from different perspectives: OC can be viewed (1) as a philosophy of adaptive and self-organising - life-like - technical systems, (2) as an approach to a more quantitative and formal understanding of such systems, and finally (3) a construction method for the practitioner who wants to build such systems. In this book, we first try to convey to the reader a feeling of the special character of natural and technical self-organising and adaptive systems through a large number of illustrative examples. Then we discuss quantitative aspects of such forms of organisation, and finally we turn to methods of how to build such systems for practical applications.
1 Motivation
1(12)
1.1 The Complexity Challenge
2(4)
1.2 Our Solution: Organic Computing
6(1)
1.3 The Content of This Book
7(6)
References
10(3)
2 Self-Organised Order: Examples
13(66)
2.1 Patterns and Order
14(13)
2.1.1 Order as a Macroscopic Effect
14(3)
2.1.2 Order as a Result of Simple Rules
17(2)
2.1.3 Order in Terms of Synchronisation
19(3)
2.1.4 Undesired Order as a Result of Resonance Frequency
22(2)
2.1.5 Order as a Reinforcement Effect
24(1)
2.1.6 Some Conclusions
25(2)
2.2 A Deeper Analysis of Some Self-Organised Emergent Systems...
27(44)
2.2.1 Balinese Water Temples
28(6)
2.2.2 Ants
34(10)
2.2.3 Self-Organised Traffic
44(6)
2.2.4 Artificial Life and Tierra
50(3)
2.2.5 Sorting and Clustering
53(7)
2.2.6 Cellular Automata
60(5)
2.2.7 Internet, Small World
65(6)
2.3 Common Characteristics
71(8)
References
73(6)
3 Systems
79(28)
3.1 Systems Thinking
80(5)
3.2 What is a System?
85(4)
3.2.1 Abstraction
86(1)
3.2.2 System Boundary
86(1)
3.2.3 Some System Types and Properties
87(2)
3.3 Organising Complexity: Hierarchies and Holarchies
89(18)
3.3.1 Complexity
89(6)
3.3.2 Hierarchy
95(1)
3.3.3 Holarchy
96(8)
References
104(3)
4 Quantitative Organic Computing
107(64)
4.1 Quantitative Self-Organisation
108(10)
4.1.1 Introduction
108(1)
4.1.2 Autonomy: Is the System Self-Organising or Not?
109(3)
4.1.3 Distribution of the Control Mechanism
112(3)
4.1.4 Self-Organisation, Order, and Emergence
115(1)
4.1.5 Self-Organisation as a Process
115(3)
4.2 Quantitative Emergence
118(19)
4.2.1 What Is Emergence?
119(1)
4.2.2 Measuring Order
120(5)
4.2.3 Observation Model
125(1)
4.2.4 Emergence
126(2)
4.2.5 Limitations
128(1)
4.2.6 Redundancy and Emergence
128(2)
4.2.7 Pragmatic Information
130(1)
4.2.8 Flocking Experiments
131(3)
4.2.9 Prediction
134(2)
4.2.10 Refinement and Extension
136(1)
4.3 The Survival Cycle
137(8)
4.4 Quantitative Robustness
145(13)
4.4.1 Introduction and Motivation
146(1)
4.4.2 Two Corresponding System Behaviour Descriptions
147(2)
4.4.3 Passive and Active Robustness
149(2)
4.4.4 Effective Utility Degradation
151(1)
4.4.5 Interpretation by Mechanical Analogy
152(1)
4.4.6 Robustness
153(5)
4.5 Quantitative Autonomy
158(5)
4.5.1 Semi-autonomy
158(2)
4.5.2 Configuration Space and Variability
160(1)
4.5.3 Control Flows
161(1)
4.5.4 Corrective Control
161(1)
4.5.5 Static Degree of Autonomy
162(1)
4.5.6 Dynamic Degree of Autonomy
162(1)
4.6 Controlled Emergence
163(8)
4.6.1 Semi-autonomy and Yoyo Design
164(1)
4.6.2 Goals
165(1)
4.6.3 Controlled Emergence and Controlled Self-Organisation
165(2)
4.6.4 Micro to Macro to Micro
167(1)
References
167(4)
5 Building Organic Computing Systems
171(88)
5.1 Single Context-Aware Adaptive Systems
172(23)
5.1.1 Requirements for Self-Adaptive Architectures
173(3)
5.1.2 Feedback Control
176(2)
5.1.3 The Generic Observer/Controller Architecture
178(3)
5.1.4 Alternative Concepts
181(4)
5.1.5 Distribution Variants
185(2)
5.1.6 Refining the Concept: The Multi-Level Observer/Controller Framework
187(8)
5.2 Open Collective Systems and Social Agents
195(14)
5.2.1 Open Collective Systems
196(2)
5.2.2 Social Mechanisms in Technical Systems
198(5)
5.2.3 Trust Communities as Self-Organised Social Infrastructures
203(2)
5.2.4 Negative Emergent Behaviour
205(1)
5.2.5 Action Guidance
205(4)
5.3 Goal-Oriented Holonic Systems
209(50)
5.3.1 Introduction and Motivation
211(1)
5.3.2 Challenges and Requirements for Complex Organic Systems
212(5)
5.3.3 Goals as Reference Points in Organic Systems
217(11)
5.3.4 Holonic System Structuring with Specific Properties for Complexity Management
228(5)
5.3.5 Goal-Oriented Holonics for Complex Organic Systems
233(18)
5.3.6 Conclusions
251(2)
References
253(6)
6 Design-Time to Runtime
259(26)
6.1 The Design Process
260(5)
6.2 Some Consequences of a Runtime Design Process
265(4)
6.3 A Fundamental Change in Design Processes
269(1)
6.4 OC Meta Design
270(9)
6.4.1 Some Important Terminology
271(1)
6.4.2 Organic Capabilities
272(3)
6.4.3 Design Decisions
275(2)
6.4.4 Design Phases
277(2)
6.5 Some Conclusions
279(6)
References
282(3)
7 Basic Methods
285(144)
7.1 Reaction Learning
287(41)
7.1.1 Introduction
288(3)
7.1.2 Basic Techniques
291(15)
7.1.3 Extended Classifier System
306(15)
7.1.4 Practical Considerations
321(7)
7.2 Model Learning
328(23)
7.2.1 Introduction
329(3)
7.2.2 Generative Modelling with Probabilistic Techniques
332(8)
7.2.3 Novelty and Anomaly Detection and Appropriate Model Adaptation
340(3)
7.2.4 Highly Autonomous Model Learning in OC Systems
343(3)
7.2.5 Application in (Distributed) Intrusion Detection Systems
346(3)
7.2.6 Summary
349(2)
7.3 Optimisation
351(34)
7.3.1 Recap: Optimisation within the MLOC
352(5)
7.3.2 Problem Definition
357(1)
7.3.3 Basic Definition
357(3)
7.3.4 Termination Criterion
360(1)
7.3.5 Constraints
361(2)
7.3.6 Uncertainty About the True System State
363(2)
7.3.7 Robustness
365(2)
7.3.8 Some Optimisation Difficulties
367(2)
7.3.9 Further Problem Types
369(4)
7.3.10 Some Optimisation Heuristics
373(12)
7.4 Influence Detection
385(20)
7.4.1 Negative Consequences of Unhandled Influences
387(2)
7.4.2 Approach for Measuring Mutual Influences
389(6)
7.4.3 Applying Influence Measures
395(1)
7.4.4 Application of the Methodology
396(8)
7.4.5 Conclusion
404(1)
7.5 Interaction
405(24)
7.5.1 Introduction
406(1)
7.5.2 Consensus Problems
407(5)
7.5.3 Negotiation Techniques
412(8)
7.5.4 Where Interaction Schemes Are Used in OC
420(1)
References
420(9)
8 Applications
429(120)
8.1 Urban Traffic Control and Management
429(18)
8.1.1 Urban Traffic Control and Management
430(1)
8.1.2 Self-adaptive Traffic Light Control
431(4)
8.1.3 Self-organised Coordination Schemes
435(4)
8.1.4 Self-organised Route Guidance
439(5)
8.1.5 Conclusion
444(3)
8.2 Data Communication Networks
447(23)
8.2.1 Self-adaptive Protocol Parameter Adaptation
450(10)
8.2.2 Knowledge Exchange
460(3)
8.2.3 Self-organised Cooperative Parameter Optimisation
463(4)
8.2.4 Conclusion
467(3)
8.3 Electric Power Management
470(16)
8.3.1 Challenges in Decentralised Power Management
472(3)
8.3.2 Approaches to Self-organising Power Systems
475(1)
8.3.3 Self-organising Autonomous Virtual Power Plants
476(7)
8.3.4 Conclusion
483(3)
8.4 Distributed Smart Cameras
486(10)
8.4.1 System Model
487(1)
8.4.2 Self-organising Spatial Partitioning
488(3)
8.4.3 Distributed Multi-Camera Tracking
491(2)
8.4.4 Robustness
493(2)
8.4.5 Conclusion
495(1)
8.5 Trust Communities in Open Distributed Systems
496(19)
8.5.1 Open Distributed Systems
497(3)
8.5.2 Implicit Trust Community (iTC)
500(2)
8.5.3 Explicit Trust Community (eTC)
502(3)
8.5.4 Normative Trust Community (nTC)
505(1)
8.5.5 Application Distributed Low-Power Sensor Network
506(3)
8.5.6 Application Open Grid Computing
509(3)
8.5.7 Application Distributed Rendering
512(2)
8.5.8 Conclusion
514(1)
8.6 Online Optimisation for Parallel Robots
515(10)
8.6.1 Self-* Properties of Industrial Robots
516(1)
8.6.2 Serial and Parallel Mechanisms
517(4)
8.6.3 Optimisation at Runtime
521(4)
8.7 Swarm Robotics
525(24)
8.7.1 Introduction
526(2)
8.7.2 Example Tasks
528(1)
8.7.3 Micro-Macro Problem and Local Sampling
529(2)
8.7.4 Modelling Approaches
531(1)
8.7.5 Collective Decision-Making
532(2)
8.7.6 Example Implementations and Projects
534(1)
References
535(14)
9 The Major Context
549(24)
9.1 Introduction
550(1)
9.2 A Brief History of Organic Computing
551(2)
9.3 A Philosophical Foundation: The General Systems Theory
553(3)
9.3.1 Scope of the General System Theory
554(1)
9.3.2 General System Theory and OC
555(1)
9.4 A Stimulating Environment: Cybernetics
556(2)
9.5 A Prominent Predecessor: Pro-Active Computing
558(3)
9.6 A Close Companion: Autonomic Computing
561(2)
9.7 An Agent Perspective: Multi-Agent Systems
563(2)
9.8 A Focus on Collective Behaviour: Complex Adaptive Systems
565(1)
9.9 A Vision of Interacting Collections: Ubiquitous and Pervasive Computing
565(1)
9.10 A Focus on (Self-) Integration: Wrappings
566(2)
9.11 Some Conclusions from Related Approaches
568(5)
References
570(3)
10 Outlook
573
References
576
C. Müller-Schloer, Institut für Systems Engineering -  System- und RechnerArchitektur (SRA), Leibniz Universität Hannover





S. Tomforde, Universität Kassel, Intelligent Embedded Systems
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