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C O N T E N T S

1 i n t r o d u c t i o n 13

1 . 1 Original contributions 15

1 . 2 Other contributions not related to this thesis 16 1 . 3 Thesis layout 20

2 t h e d e s i g n p r o b l e m 23 2 . 1 Swarm systems 23

2 . 2 Engineering swarm systems 25 2 . 2 . 1 Micro to macro 26 2 . 2 . 2 Macro to micro 29

2 . 2 . 3 Discussion of engineering methods for swarm sys- tems 32

3 d e s i g n pat t e r n s a s a n e n g i n e e r i n g m e t h o d o l o g y f o r d e c e n t r a l i s e d s y s t e m s 35

3 . 1 A catalogue of reusable solutions 36 3 . 2 The design pattern and its attributes 37 3 . 3 How to build a design pattern 38

4 t o wa r d s t h e f o r m a l i s at i o n o f a d e s i g n pat t e r n f o r d e c e n t r a l i s e d d e c i s i o n m a k i n g 41

4 . 1 From macroscopic descriptions to implementation guide- lines 42

4 . 1 . 1 The macroscopic model 43 4 . 1 . 2 Implementation guidelines 44

4 . 2 Case study: shortest path discovery/selection 47 4 . 2 . 1 Problem definition 48

4 . 2 . 2 Implementation of abstract multiagent simula- tions 49

4 . 2 . 3 Implementation of physics-based swarm robotics simu- lations 55

4 . 3 Results 58

4 . 3 . 1 Abstract multiagent simulations 58

4 . 3 . 2 Physics-based swarm robotics simulations 64 4 . 4 Discussion 68

5 a d e s i g n pat t e r n f o r d e c e n t r a l i s e d d e c i s i o n m a k - i n g 71

5 . 1 Name 71

9

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10 Contents

5 . 2 Problem 71 5 . 3 Context 72

5 . 4 Design rationale 72 5 . 5 Solution 73

5 . 5 . 1 Macroscopic description: infinite-size, deterministic, time/state-space continuous 73

5 . 5 . 2 Macroscopic description: finite-size, stochastic, time continuous, state-space discrete 74

5 . 5 . 3 Microscopic description: agent-based, stochastic, time/state-space discrete 76

5 . 5 . 4 Implementation guidelines 78

5 . 5 . 4 . 1 Population size dependent probabilities 78 5 . 5 . 4 . 2 Homogeneous versus heterogeneous imple-

mentation 79

5 . 5 . 4 . 3 Latent and interactive agents 81 5 . 5 . 4 . 4 Minimum speed of the process 82 5 . 5 . 4 . 5 Dealing with episodic discovery 83 5 . 5 . 4 . 6 Skeleton of the agent behaviour 84 6 c a s e s t u d i e s 87

6 . 1 Metrics 87

6 . 2 Case study I: Collective decisions in a fully-connected multia- gent system 89

6 . 2 . 1 Case study I-A 89 6 . 2 . 2 Case study I-B 91

6 . 3 Case study II: Collective decisions in a search and exploitation problem 97

7 c o n c l u s i o n s 103 b i b l i o g r a p h y 107

Appendices 123

a s u r v i va l a na ly s i s 125

b s ta b i l i t y a na ly s i s a n d pa r a m e t e r i s at i o n c h o i c e f o r

c a s e s t u d y i - a 129

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