A framework of evaluating partitioning mechanisms for agent-based simulation systems

For an agent-based system that aims to simulate a large number of agents under different scenarios, a proper partitioning mechanism is an essential factor to the performance of the system. However, there is still an issue on selecting/developing an appropriate partitioning mechanism for a specific agent-based simulation system. One way is to implement distributed agent-based systems using multiple partitioning mechanisms and to compare their performance. However this is in general not feasible. In this paper, we propose a generic framework and a simple evaluation process to address the issue of selecting an appreciate partitioning mechanism by simulating the effect of the partitioning mechanism to predict its performance for an agent-based simulation system.

[1]  Yuefan Deng,et al.  An Adaptive Load Balancing Method for Parallel Molecular Dynamics Simulations , 2000 .

[2]  Wentong Cai,et al.  Agent‐based human behavior modeling for crowd simulation , 2008, Comput. Animat. Virtual Worlds.

[3]  Wentong Cai,et al.  A parallelism analyzer algorithm for a conservative super-step simulation protocol , 1999, Proceedings of the 32nd Annual Hawaii International Conference on Systems Sciences. 1999. HICSS-32. Abstracts and CD-ROM of Full Papers.

[4]  Sandeep Singhal,et al.  Networked virtual environments , 1999 .

[5]  Mikel D. Petty,et al.  Developing a Crowd Federate for Military Simulation , 2004 .

[6]  Kevin O'Brien,et al.  Human Behavior Models for Agents in Simulators and Games: Part I: Enabling Science with PMFserv , 2006, Presence: Teleoperators & Virtual Environments.

[7]  Michael Zyda,et al.  Networked virtual environments - desgin and implementation , 1999 .

[8]  G SilvermanBarry,et al.  Human behavior models for agents in simulators and games , 2006 .

[9]  Mauricio G. C. Resende,et al.  Greedy Randomized Adaptive Search Procedures , 1995, J. Glob. Optim..

[10]  Demetri Terzopoulos,et al.  Autonomous pedestrians , 2007, Graph. Model..

[11]  Moon-Jung Chung,et al.  Predicting the performance of synchronous discrete event simulation , 2004, IEEE Transactions on Parallel and Distributed Systems.

[12]  John C. S. Lui,et al.  An Efficient Partitioning Algorithm for Distributed Virtual Environment Systems , 2002, IEEE Trans. Parallel Distributed Syst..

[13]  Randy L. Haupt,et al.  Practical Genetic Algorithms , 1998 .

[14]  Wentong Cai,et al.  Performance prediction tools for parallel discrete-event simulation , 1999, Proceedings Thirteenth Workshop on Parallel and Distributed Simulation. PADS 99. (Cat. No.PR00155).

[15]  Gnana Bharathy,et al.  Human Behavior Models for Agents in Simulators and Games: Part II: Gamebot Engineering with PMFserv , 2006, Presence: Teleoperators & Virtual Environments.

[16]  Silvia Rueda,et al.  A Latency-Aware Partitioning Method for Distributed Virtual Environment Systems , 2007, IEEE Transactions on Parallel and Distributed Systems.

[17]  Michael Gleicher,et al.  Scalable behaviors for crowd simulation , 2004, Comput. Graph. Forum.

[18]  Luca Maria Gambardella,et al.  Ant colony system: a cooperative learning approach to the traveling salesman problem , 1997, IEEE Trans. Evol. Comput..

[19]  D. E. Keyes,et al.  1. Domain Decomposition in the Mainstream of Computational Science , 2002 .

[20]  Daniel Thalmann,et al.  A high-level architecture for believable social agents , 2000, Virtual Reality.