Agent coordination mechanisms for solving a partitioning task:
Saved in:
| Main Author: | |
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| Format: | Thesis Book |
| Language: | English |
| Published: |
Berlin
Logos-Verl.
2006 [erschienen] 2007
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| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Physical Description: | 184 S. graph. Darst. |
| ISBN: | 9783832514839 383251483X |
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MARC
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Record in the Search Index
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| adam_text | Contents
1 Introduction 1
1.1 Organization of this Thesis 2
1.2 Taxonomy of the approaches 3
1.3 Empirical Analysis 4
2 Definitions and Communication Graphs S
2.1 Terms and Notation 5
2.1.1 Agent 5
2.1.2 Distributed Artificial Intelligence 7
2.1.3 Self-Organisation 15
2.1.4 Stigmergy 18
2.1.5 Evolutionary Algorithms 20
2.2 Problem Notation / Definitions 22
2.3 Neighbourhood Graphs 23
2.3.1 Introduction 23
2.3.2 Notation 25
2.3.3 fcNG in One- and Two-Dimensional Space 26
2.3.4 A;NG in Three-Dimensional Space 31
2.3.5 Graph Connectivity 36
2.3.6 Energy Efficiency 38
2.3.7 Conclusion 41
3 The Online Partitioning Problem 43
3.1 The OPP 43
3.2 Exact OPP Algorithms 45
3.2.1 Complete Algorithm for Arbitrary Parameters 46
3.2.2 Exact Algorithm for Two Targets 46
3.3 Related Problems 50
3.3.1 Pattern Formation 50
3.3.2 Dynamic Task Allocation 52
3.3.3 Graph Partitioning 52
3.3.4 Network Flows 56
3.4 Formal Model 58
4 Basic Strategies 61
4.1 Non-Communicative Partitioning Strategies 61
4.1.1 Random Target Strategy (RTS) 61
4.1.2 ID-Dependent Strategy (IDS) 62
4.1.3 Next-Target Strategy (NTS) 63
4.2 Communicative Partitioning Strategies 63
4.2.1 Neighbourhood-Based Strategies (NBS) 63
4.2.2 Border Switch Strategy (BSS) 65
4.2.3 Exchange Target Strategy (ETS) 66
4.3 Results 69
4.3.1 Abilities 69
4.3.2 Individual Strategies Tuning 70
4.3.3 Strategy Comparison 73
4.3.4 Standard Deviation 75
4.4 Moving Agents 75
4.4.1 No Effects through Reconsideration 76
4.4.2 Effects through Reconsideration 76
4.5 Conclusion 78
5 Learning Emergent Agent Behaviours 79
5.1 Related Work 80
5.2 Mapping Agents to Cellular Automata 81
5.2.1 Mapping Function 81
5.2.2 Introducing Inexactness and Limitations 84
5.2.3 Conclusion 87
5.3 The Set of Rules for the CA and EA Operators 87
5.3.1 Definitions 87
5.3.2 Evolutionary Operators and Fitness Function 88
5.3.3 Rule Similarity 89
5.3.4 Algorithm Worst Case Scenario 91
5.3.5 Simulation Environment Parameters 92
5.3.6 Parameter Tuning 93
5.3.7 Results 95
5.3.8 Conclusion 95
5.4 Memetic Individual Modifications 96
5.4.1 Local Individual- and Generation-Modifications 97
5.4.2 Results 99
5.4.3 Conclusion 101
6 Development of Organisations 103
6.1 Economic Multi Agent System 103
6.2 Schillo s Approach 104
6.3 Enhancement of Schillo s Approach 108
6.3.1 Introducing Agent Delay 108
6.3.2 Introducing Agent Generosity 108
6.3.3 Introducing Locality 110
6.4 Using Schillo s Approach to solve the OPP, Part 1 110
6.4.1 The Algorithm Ill
6.4.2 Results 112
6.5 Using Schillo s Approach to solve the OPP, Part II 115
6.5.1 The Algorithm 116
6.5.2 Results 120
6.6 Conclusion 123
7 Learning Communication 125
7.1 Introduction 125
7.2 The Static Approach 126
7.2.1 Evaluate the Quality of a Communication Structure 126
7.2.2 The Evolutionary Algorithm 127
7.2.3 Results 128
7.3 The Dynamic Approach 128
7.3.1 Our Approach 130
7.3.2 Size and Characteristics of the g-Intervals in Space 130
7.3.3 The Evolutionary Algorithm 131
7.3.4 Our Algorithm 133
7.4 Results 134
7.4.1 Parameters 134
7.4.2 Fitness Development 134
7.4.3 Limited q-Values 136
7.4.4 Development of g-Table Values 136
7.4.5 Calculation of q-Table Entries 138
7.5 Conclusion 139
8 Applications 141
8.1 WLan Router Choice 141
8.1.1 WLan Basics 142
8.1.2 Data Transfer Rate 142
8.1.3 Assigning OPP Algorithms to Access Point Selection 143
8.1.4 Results 144
8.1.5 Conclusion 149
8.2 Local Dynamic Task Allocation 150
8.2.1 Market Based Approaches 150
8.2.2 Insect Based Approaches 151
8.2.3 Mapping to Computer Science 151
8.2.4 Adopting OPP Ideas 155
8.2.5 Results 157
8.2.6 Conclusion 158
8.3 Fire Fighting with Unmanned Aerial Vehicles (UAV) 159
8.3.1 Motivation 159
8.3.2 Advantages and Mapping of OPP Algorithms 159
9 Conclusions and Future Work 161
A Proofs 165
A. 1 Maximum of Product Function 165
A.2 Properties of g-Intervals 165
B Function characteristics 167
B.I ip Function for Mapping 167
B.2 Response Probability Function 168
C Additional Simulation Results 169
C.I Final Fitness Values for Different Parameters 169
C.2 sATA and kATA Results 169
C.2.1 sATA and kATA Results for 126 tasks 170
C.2.2 sATA and kATA Results for 1008 tasks 171
C.2.3 sATA and kATA Results for 4032 tasks 172
References 173
Index 181
|
| any_adam_object | 1 |
| author | Goebels, Andreas |
| author_GND | (DE-588)132617099 |
| author_facet | Goebels, Andreas |
| author_role | aut |
| author_sort | Goebels, Andreas |
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| building | Verbundindex |
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| dewey-full | 006.3 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 006 - Special computer methods |
| dewey-raw | 006.3 |
| dewey-search | 006.3 |
| dewey-sort | 16.3 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
| format | Thesis Book |
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| indexdate | 2024-07-09T23:15:27Z |
| institution | BVB |
| isbn | 9783832514839 383251483X |
| language | English |
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| spelling | Goebels, Andreas Verfasser (DE-588)132617099 aut Agent coordination mechanisms for solving a partitioning task Andreas Goebels Berlin Logos-Verl. 2006 [erschienen] 2007 184 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Paderborn, Univ., Diss., 2006 Schwarmintelligenz (DE-588)4793676-9 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Selbstorganisation (DE-588)4126830-1 gnd rswk-swf Evolutionärer Algorithmus (DE-588)4366912-8 gnd rswk-swf Partition Informatik (DE-588)4542600-4 gnd rswk-swf Mehragentensystem (DE-588)4389058-1 gnd rswk-swf (DE-588)4113937-9 Hochschulschrift gnd-content Mehragentensystem (DE-588)4389058-1 s Schwarmintelligenz (DE-588)4793676-9 s Partition Informatik (DE-588)4542600-4 s Maschinelles Lernen (DE-588)4193754-5 s Selbstorganisation (DE-588)4126830-1 s Evolutionärer Algorithmus (DE-588)4366912-8 s DE-188 HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=022147590&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Goebels, Andreas Agent coordination mechanisms for solving a partitioning task Schwarmintelligenz (DE-588)4793676-9 gnd Maschinelles Lernen (DE-588)4193754-5 gnd Selbstorganisation (DE-588)4126830-1 gnd Evolutionärer Algorithmus (DE-588)4366912-8 gnd Partition Informatik (DE-588)4542600-4 gnd Mehragentensystem (DE-588)4389058-1 gnd |
| subject_GND | (DE-588)4793676-9 (DE-588)4193754-5 (DE-588)4126830-1 (DE-588)4366912-8 (DE-588)4542600-4 (DE-588)4389058-1 (DE-588)4113937-9 |
| title | Agent coordination mechanisms for solving a partitioning task |
| title_auth | Agent coordination mechanisms for solving a partitioning task |
| title_exact_search | Agent coordination mechanisms for solving a partitioning task |
| title_full | Agent coordination mechanisms for solving a partitioning task Andreas Goebels |
| title_fullStr | Agent coordination mechanisms for solving a partitioning task Andreas Goebels |
| title_full_unstemmed | Agent coordination mechanisms for solving a partitioning task Andreas Goebels |
| title_short | Agent coordination mechanisms for solving a partitioning task |
| title_sort | agent coordination mechanisms for solving a partitioning task |
| topic | Schwarmintelligenz (DE-588)4793676-9 gnd Maschinelles Lernen (DE-588)4193754-5 gnd Selbstorganisation (DE-588)4126830-1 gnd Evolutionärer Algorithmus (DE-588)4366912-8 gnd Partition Informatik (DE-588)4542600-4 gnd Mehragentensystem (DE-588)4389058-1 gnd |
| topic_facet | Schwarmintelligenz Maschinelles Lernen Selbstorganisation Evolutionärer Algorithmus Partition Informatik Mehragentensystem Hochschulschrift |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=022147590&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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