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http://dx.doi.org/10.9709/JKSS.2019.28.3.001

Hierarchical Agent Synthesis Framework using Discrete Event System Specification and System Entity Structure  

Choi, Changbeom (School of Global Entrepreneurship and ICT at Handong Global University)
Abstract
An agent-based simulation is a popular simulation tool to solve various problems, such as stock market, population prediction, disease prediction, and development of a traffic system. As the agents are developed and researched in different application fields, the agent has a rigid structure and may not acceptable in different domains. As a result, it is a challenging problem to define a structure for an agent structure to reflect the researcher's simulation objective. This research proposes an extendable form for an agent and its modeling environment. In order to propose a standard structure, this study adopts system entity structure and discrete event system specification formalism. Also, this research introduces the SESManager which supports the proposed specification method. The proposed environment can hierarchically define the agent structure and synthesize the agent so that it can perform the agent simulation according to the user's simulation purpose.
Keywords
Agent; Agent Modeling; Agent based Simulation; Simulation Tool;
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  • Reference
1 Bonabeau, E. (2002), Agent-based modeling: Methods and techniques for simulating human systems Proceedings of the National Academy of Sciences. May 14, 2002
2 Choi, Changbeom (2019). SESManager: System Entity Structure Manager (Version 1.0) [Software]. Available from https://github.com/cbchoi/SESManager
3 Erol, K., Levy, R., and Wentworth, J. (2007) Application of Agent Technology to Traffic Simulation, United States Department of Transportation, May 15, 2007
4 Hwang, M. et. al. (2009) Rule-Based Simulation of Multi-Cellular Biological Systems-A Review of Modeling Techniques: Cellular and Molecular Bioengineering, Vol. 2, No. 3, pp. 285-294, 2009   DOI
5 Macal, C. M., and Michael J. N. (2005) Tutorial on agent-based modeling and simulation, Proc. of the 37th conference on Winter simulation, Winter Simulation Conference, 2005.
6 Railsback, S. F., Steven L. L., and Stephen K. J. (2006), Agent-based simulation platforms: Review and development recommendations, Simulation 82.9, 609-623, 2006   DOI
7 Russell, S. J., and Norvig, P. (2016). Artificial intelligence: a modern approach. Malaysia; Pearson Education Limited,.
8 Siddiqah A. et. al. (2009). A new hybrid agent-based modeling decision support system for breast cancer research, IEEE ICICT, IBA, Karachi, August 15-16, 2009. Breast Cancer DSS
9 Young C. Kim, Kyung S. Ham, and Tag Gon Kim (1993). Object-Oriented Memory Management in DEVSim++, Proceedings of the 1993 Winter Simulation Conference, 1993.
10 Zeigler, B. P., Luh, C.J., and Kim, T.G. (1991). Model Base Management for Multifacetted Systems, ACM Transactions on Modeling and Computer Simulation - TOMACS, Vol. 1, No. 3, pp. 195-218, 1991.   DOI
11 Zeigler, B. P., Praehofer, H. and Kim, T.G. (2000). Theory of Modelling and Simulation (2nd Edition), Academic Press, 2000.
12 Zeigler, B. P., Chung, Kim Do (2013). System Entity Structures for Suites for Simulation Models, International Journal of Modeling, Simulation, and Scientific Computing 4(3), 2013.
13 Zeigler, B.P., Sarjoughian, H. S. (2017). Guide to Modeling and Simulation of Systems of Systems (2nd Edition), Springer International Publishing, 2017.