Complexities of simulating a hybrid agent-landscape model using multi-formalism composability

ABSTRACT

Hybrid agent-landscape models are used as an environment in which to study humans, the environment, and their dynamics. To provide flexibility in model design, expressiveness, and modification, the environment models and human agent models should be developed independently. While retaining each model's individuality, the models can be composed to create a model of a complex, hybrid agent-landscape system. This should allow for a much more in-depth analysis of each model independently, as well as a study of their interactions. To create such a modeling environment requires a look beyond a simple interface between two models. It may require that the models' formalisms be composed, their execution be synchronized, their architectures be integrated, and a common visualization be created to provide a whole-system data view during simulation. This paper discusses the complexities of such an undertaking.

REFERENCES

• 1: MEDLAND. 2005. "Landuse and Landscape Socioecology in the Mediterranean Basin: A Natural Laboratory for the Study of the Long-Term Interaction of Human and Natural Systems". http://www.asu.edu/clas/shesc/projects/medland/ (accessed December 2006).
• 2: Davis, P. K. and R. H. Anderson. 2004. "Improving the Composability of DoD Models and Simulations". The Journal of Defense Modeling and Simulation. 1(5), pp. 5 -- 17.
• 3: Hessam S. Sarjoughian, Model composability, Proceedings of the 38th conference on Winter simulation, December 03-06, 2006, Monterey, California
• 4: Paul A. Fishwick, Simulation Model Design and Execution: Building Digital Worlds, Prentice Hall PTR, Upper Saddle River, NJ, 1995
• 5: Zeigler, B. P., H. Praehofer, and T. G. Kim. 2000. Theory of Modeling and Simulation: Integrated Discrete Event and Continuous Complex Dynamic Systems, 2nd ed. Academic Press. San Diego, CA.
• 6: Hall, S. 1998. Personal Communication.
• 7: Hall, S. 2005. "Learning in a Complex Adaptive System for ISR Resource Management". Spring Simulation Conference.
• 8: Zeigler, B. P. 2006. "DEVS&DESS in DEVS". DEVS Integrative Modeling & Simulation Symposium. Huntsville, Alabama.
• 9: IEEE. 2000. HLA Framework and Rules. IEEE 1516--2000.
• 10: North, M., T. Howe, N. Collier, and J. Vos. 2005. "The Repast Symphony Development Environment". In Proceedings of the Agent 2005 Conference on Generative Social Processes, Models, and Mechanisms. Chicago, IL, USA.
• 11: North, M., T. Howe, N. Collier, and J. Vos. 2005. "The Repast Symphony Runtime System". In Proceedings of the Agent 2005 Conference on Generative Social Processes, Models, and Mechanisms. Chicago, IL, USA.
• 12: Gary W. Godding , Hessam S. Sarjoughian , Karl G. Kempf, Multi-formalism modeling approach for semiconductor supply/demand networks, Proceedings of the 36th conference on Winter simulation, December 05-08, 2004, Washington, D.C.
• 13: Parker, D. C., S. M. Manson, M. A. Janssen, M. J. Hoffmann, and P. Deadman. 2003. "Multi-Agent Systems for the Simulation of Land-Use and Land-Cover Change: A Review". Annals of the Association of American Geographers. 93(2), 314--337.
• 14: Swarm. 1996. Swarm Simulation System. Swarm Development Group. Available from http://www.santafe.edu/projects/swarm/ (accessed December 2006).
• 15: R. Minson , G. K. Theodoropoulos, Distributing RePast agent-based simulations with HLA, Concurrency and Computation: Practice & Experience, v.20 n.10, p.1225-1256, July 2008 [doi>10.1002/cpe.v20:10]
• 16: Ptolemy Project. 2006. Ptolemy II. Available from http://ptolemy.berkeley.edu/ptolemyII/main.htm (accessed December 2006)
• 17: Luca de Alfaro , Thomas A. Henzinger, Interface automata, Proceedings of the 8th European software engineering conference held jointly with 9th ACM SIGSOFT international symposium on Foundations of software engineering, September 10-14, 2001, Vienna, Austria
• 18: Sarjoughian, H. S. and J. Plummer. 2002. Design and Implementation of a Bridge between RAP and DEVS. Internal Report, Computer Science and Engineering, Arizona State University. pp. 1--26.
• 19: Hessam Sarjoughian , Dongping Huang, A Multi-Formalism Modeling Composability Framework: Agent and Discrete-Event Models, Proceedings of the 9th IEEE International Symposium on Distributed Simulation and Real-Time Applications, p.249-256, October 10-11, 2005 [doi>10.1109/DISTRA.2005.4]
• 20: Hessam S. Sarjoughian , Dongping Huang , Gary W. Godding , Karl G. Kempf , Wenlin Wang , Daniel E. Rivera , Hans D. Mittelmann, Hybrid discrete event simulation with model predictive control for semiconductor supply-chain manufacturing, Proceedings of the 37th conference on Winter simulation, December 04-07, 2005, Orlando, Florida
• 21: Mayer, G. R., H. S. Sarjoughian, E. K. Allen, S. Falconer, and M. Barton. 2006. Simulation Modeling for Human Community and Agricultural Landuse. In Proceedings of the 2006 Spring Simulation Conference, pp. 65--72, SCS. Huntsville, AL, USA.
• 22: GRASS. 2004. Geographic Resources Analysis Support System. http://grass.itc.it/ (accessed December 2006).
• 23: Markus Neteler , Helena Mitasova, Open Source GIS: A GRASS GIS Approach, Springer-Verlag New York, Inc., Secaucus, NJ, 2007
• 24: Burrough, P. A. and R. A. McDonnell. 1998. Principles of Geographic Information Systems. Oxford University Press Inc. New York, NY.

INDEX TERMS

Keywords:
agents, cellular automata, multi-formalism, multi-modeling, poly-formalism