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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.
List of contents
Introduction.- Preliminaries and related work: Agent-based modeling and simulation, Parallel and distributed multi-agent simulation, Summary.- E ective and e cient model development: The need for a reference model for agent-based modeling and simulation, GRAMS - General Reference Model for Agent-based Modeling and Simulation, Summary.- E ective model execution: Model partitioning and multi-level parallelization, Example implementation of GRAMS, Summary.- Conclusions.
About the author
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.
Summary
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.
