Modeling and Simulation of Complex Systems (eBook)

A Framework for Efficient Agent-Based Modeling and Simulation

Robert Siegfried (Autor)

eBook Download: PDF

2014 | 2014
XIX, 227 Seiten
Springer Fachmedien Wiesbaden GmbH (Verlag)
9783658075293 (ISBN)

Infos

Robert Siegfried presents a framework for efficient agent-based modeling and simulation of complex systems. He compares different approaches for describing structure and dynamics of agent-based models in detail. Based on this evaluation the author introduces the 'General Reference Model for Agent-based Modeling and Simulation' (GRAMS). Furthermore he presents parallel and distributed simulation approaches for execution of agent-based models -from small scale to very large scale. The author shows how agent-based models may be executed by different simulation engines that utilize underlying hardware resources in an optimized fashion.

Robert Siegfried is Senior Consultant for IT/M&S projects. He earned his doctorate in modeling and simulation at the Universität der Bundeswehr München. His research areas are agent-based modeling and simulation, distributed simulation, and quality management. He has worked on topics like model documentation and management, distributed simulation test beds, and process models. He is active member of the NATO Modeling and Simulation Group and the Simulation Interoperability Standards Organization.

Inhalt

Abstract 6
Zusammenfassung 8
Contents 10
List of Figures 15
List of Tables 17
1 Introduction 18
1.1 Motivation 18
1.2 Goals of this thesis 20
Effective and efficient model development 20
Effective model execution 22
1.3 Approach of this thesis and outline 23
Part I Preliminaries and related work 26
2 Agent-based modeling and simulation 27
2.1 Basic terminology 27
2.1.1 Complex system 27
2.1.2 Model 28
2.1.3 Model development process 29
2.1.4 Simulation 31
2.2 Agent-based modeling and simulation 33
2.2.1 Agent 33
2.2.2 Agent architecture 35
2.2.3 Relationships with other modeling paradigms 37
2.2.4 Benefits and drawbacks of agent-based modeling and simulation 39
2.3 Related work 41
2.3.1 Klügl (2007) 42
2.3.2 Scheutz, Schermerhorn (2006) 46
2.3.3 Ferber, Müller (1996) and Weyns, Holvoet (2003) 49
2.3.4 DEVS and variable structure models 52
2.3.5 Agent-Object-Relationship Simulation 53
2.3.6 Further related work 54
2.3.7 Summary 56
2.4 The role of the environment in agent-based models 60
2.4.1 Definition and properties 61
2.4.2 Dynamics 62
3 Parallel and distributed multi-agent simulation 64
3.1 Basic terminology 64
3.2 Motivation and requirements with respect to multi-agent simulation 66
3.3 Existing approaches and their limitations 68
3.3.1 Pawlaszczyk (2009) 69
3.3.2 Logan, Theodoropoulos (2001) 70
3.3.3 P. Riley, G. Riley (2003) 70
3.3.4 Scheutz, Schermerhorn (2006) 71
3.3.5 Aaby et al. (2010) 72
3.3.6 Further related work 73
4 Summary 76
Part II Effective and efficient model development 78
5 The need for a reference model for agent-based modeling and simulation 79
5.1 Current problems and needs of agent-based modeling and simulation 79
5.1.1 Weak definitions of basic terms and concepts 79
5.1.2 Bad support of collaborative development 81
5.1.3 Need for improvement of quality by standardization 82
5.2 General purpose of reference models 82
5.3 Requirements for a reference model 83
5.3.1 Clear distinction between model and simulation engine 84
5.3.2 Simulated environment 85
5.3.3 Simulation time 85
5.3.4 Agents and objects 86
5.3.5 Actions, effects and constraints 86
5.3.6 Abstraction from specific agent architectures 87
5.3.7 Formal specification 87
6 GRAMS – General Reference Model for Agent-based Modeling and Simulation 89
6.1 Aims and target audience 89
6.2 Basic idea of the GRAMS reference model 90
6.3 Definition of an agent-based model 92
6.4 Macro-level modeling 95
6.4.1 Simulation time 95
6.4.2 Environment 96
6.4.3 Embedding of entities 98
6.5 Events 99
6.6 Micro-level modeling 102
6.6.1 Attributes of agents and objects 103
6.6.2 Sensors and effectors 104
6.6.3 State of an agent 109
6.6.4 Constraints 109
6.6.5 Behavior of an agent 112
6.7 Simulation of an agent-based model 114
6.8 Application areas of the GRAMS reference model 116
6.9 Evaluation 118
6.9.1 Mapping of basic agent architectures 118
6.9.2 Case studies 122
7 Summary 125
Part III Effective model execution 128
8 Model partitioning and multi-level parallelization 129
8.1 Model partitioning strategies 129
8.1.1 Terms and definitions 129
8.1.2 Conflict detection as bottleneck 132
8.1.3 Partitioning strategy: node-level, per agent, static 134
8.1.4 Partitioning strategy: node-level, per agent, dynamic 135
8.1.5 Partitioning strategies on cluster-level 139
8.2 Parallel execution 141
8.2.1 Parallelization on nodeand processor-level 142
8.2.2 Parallelization on cluster-level 143
8.2.3 Multi-level parallelization 143
8.3 Reliability of a simulation environment 144
9 Example implementation of GRAMS 145
9.1 Overview 145
9.2 Representation of an agent-based model 148
9.2.1 Simulation time 149
9.2.2 Environment 150
9.2.3 Events 151
9.2.4 Constraints 152
9.2.5 Entities 154
9.3 Simulation engines 155
9.3.1 Single-threaded event-driven simulation engine 156
9.3.2 Single-threaded time-stepped simulation engine 158
9.3.3 Parallelization on node-level 159
9.3.4 Parallelization on cluster-level 163
9.4 Test and verification 163
9.5 Empirical measurements 166
9.5.1 Simulation model used for benchmarks 166
9.5.2 Benchmark suite 0: Dependency between simulation time and runtime 170
9.5.3 Benchmark suites 1 and 2: Dependency between number of agents and runtime 172
9.5.4 Benchmark suite 3: Parallelization on node-level 177
10 Summary 180
Part IV Conclusions 183
11 Conclusions and outlook 184
11.1 Summary 184
11.2 Scientific contributions and applicability 185
11.3 Outlook 188
Part V Appendix 189
A Case studies of the GRAMS reference model 190
A.1 Case study: Warehouse 190
A.1.1 Structured problem description 190
A.1.2 Conceptual model 198
A.2 Case study: Joint Fire Support 211
Bibliography 213

Produkt-Details

• Erscheint lt. Verlag: 8.10.2014
• Zusatzinfo: XIX, 227 p. 40 illus., 13 illus. in color.
• Verlagsort: Wiesbaden
• Sprache: englisch
• Themenwelt: Mathematik / Informatik ► Informatik
• Schlagworte: Agent-based model • GRAMS • multi-agent simulation • multi-level parallelization • Simulation of Complex Systems
• ISBN-13: 9783658075293 / 9783658075293