Modeling and Simulation of Complex Systems 1st edition by Robert Siegfried ISBN 3658075295 9783658075293

1 Introduction

1.1 Motivation

1.2 Goals of this thesis

Effective and efficient model development

Effective model execution

1.3 Approach of this thesis and outline

Part I Preliminaries and related work

2 Agent-based modeling and simulation

2.1 Basic terminology

2.1.1 Complex system

2.1.2 Model

2.1.3 Model development process

2.1.4 Simulation

2.2 Agent-based modeling and simulation

2.2.1 Agent

2.2.2 Agent architecture

2.2.3 Relationships with other modeling paradigms

2.2.4 Benefits and drawbacks of agent-based modeling and simulation

2.3 Related work

2.3.1 Klügl (2007)

2.3.2 Scheutz, Schermerhorn (2006)

2.3.3 Ferber, Müller (1996) and Weyns, Holvoet (2003)

2.3.4 DEVS and variable structure models

2.3.5 Agent-Object-Relationship Simulation

2.3.6 Further related work

2.3.7 Summary

2.4 The role of the environment in agent-based models

2.4.1 Definition and properties

2.4.2 Dynamics

3 Parallel and distributed multi-agent simulation

3.1 Basic terminology

3.2 Motivation and requirements with respect to multi-agent simulation

3.3 Existing approaches and their limitations

3.3.1 Pawlaszczyk (2009)

3.3.2 Logan, Theodoropoulos (2001)

3.3.3 P. Riley, G. Riley (2003)

3.3.4 Scheutz, Schermerhorn (2006)

3.3.5 Aaby et al. (2010)

3.3.6 Further related work

4 Summary

Part II Effective and efficient model development

5 The need for a reference model for agent-based modeling and simulation

5.1 Current problems and needs of agent-based modeling and simulation

5.1.1 Weak definitions of basic terms and concepts

5.1.2 Bad support of collaborative development

5.1.3 Need for improvement of quality by standardization

5.2 General purpose of reference models

5.3 Requirements for a reference model

5.3.1 Clear distinction between model and simulation engine

5.3.2 Simulated environment

5.3.3 Simulation time

5.3.4 Agents and objects

5.3.5 Actions, effects and constraints

5.3.6 Abstraction from specific agent architectures

5.3.7 Formal specification

6 GRAMS – General Reference Model for Agent-based Modeling and Simulation

6.1 Aims and target audience

6.2 Basic idea of the GRAMS reference model

6.3 Definition of an agent-based model

6.4 Macro-level modeling

6.4.1 Simulation time

6.4.2 Environment

6.4.3 Embedding of entities

6.5 Events

6.6 Micro-level modeling

6.6.1 Attributes of agents and objects

6.6.2 Sensors and effectors

6.6.3 State of an agent

6.6.4 Constraints

6.6.5 Behavior of an agent

6.7 Simulation of an agent-based model

6.8 Application areas of the GRAMS reference model

6.9 Evaluation

6.9.1 Mapping of basic agent architectures

6.9.2 Case studies

7 Summary

Part III Effective model execution

8 Model partitioning and multi-level parallelization

8.1 Model partitioning strategies

8.1.1 Terms and definitions

8.1.2 Conflict detection as bottleneck

8.1.3 Partitioning strategy: node-level, per agent, static

8.1.4 Partitioning strategy: node-level, per agent, dynamic

8.1.5 Partitioning strategies on cluster-level

8.2 Parallel execution

8.2.1 Parallelization on nodeand processor-level

8.2.2 Parallelization on cluster-level

8.2.3 Multi-level parallelization

8.3 Reliability of a simulation environment

9 Example implementation of GRAMS

9.1 Overview

9.2 Representation of an agent-based model

9.2.1 Simulation time

9.2.2 Environment

9.2.3 Events

9.2.4 Constraints

9.2.5 Entities

9.3 Simulation engines

9.3.1 Single-threaded event-driven simulation engine

9.3.2 Single-threaded time-stepped simulation engine

9.3.3 Parallelization on node-level

9.3.4 Parallelization on cluster-level

9.4 Test and verification

9.5 Empirical measurements

9.5.1 Simulation model used for benchmarks

9.5.2 Benchmark suite 0: Dependency between simulation time and runtime

9.5.3 Benchmark suites 1 and 2: Dependency between number of agents and runtime

9.5.4 Benchmark suite 3: Parallelization on node-level

10 Summary

Part IV Conclusions

11 Conclusions and outlook

11.1 Summary

11.2 Scientific contributions and applicability

11.3 Outlook

Part V Appendix

A Case studies of the GRAMS reference model

A.1 Case study: Warehouse

A.1.1 Structured problem description

A.1.2 Conceptual model

A.2 Case study: Joint Fire Support

Bibliography