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Simulation-based engineering of complex systems: simulation-based engineering of complex systems using EXTEND+MFG+OpeMCSS
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Source Winter Simulation Conference archive
Proceedings of the 34th conference on Winter simulation: exploring new frontiers table of contents
San Diego, California
SESSION: Advanced tutorials table of contents
Pages: 147 - 156  
Year of Publication: 2002
ISBN:0-7803-7615-3
Author
John R. Clymer  California State University, Fullerton, Fullerton, CA
Sponsors
IEEE/CS : Institute of Electrical and Electronics Engineers/Computer Society
ASA : American Statistical Association
IEEE/SMCS : Institute of Electrical and Electronics Engineers/Systems, Man, and Cybernetics Society
INFORMS/CS : Institute for Operations Research and the Management Sciences/College on Simulation
NIST : National Institute of Standards and Technology
ACM: Association for Computing Machinery
(SCS) : The Society for Modeling and Simulation International
SIGSIM: ACM Special Interest Group on Simulation and Modeling
IIE : Institute of Industrial Engineers
Publisher
Winter Simulation Conference 
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ABSTRACT

A Complex Adaptive System (CAS) is a network of self-organizing, intelligent agents that share knowledge and adapt their operations in order to achieve overall system goals. Three things are needed to understand, design, and evaluate CAS. First, a mathematical model or way-of-thinking about CAS, called Context-Sensitive Systems (CSS) theory, is required to provide a solid foundation upon which to represent and describe the kinds of interactions that occur among the CAS agents during system operation. Second, a graphical modeling language is required that implements CSS theory in a way that enhances visualization and understanding of CAS. Third, a systems design and evaluation tool is required that makes it easy to apply CSS theory, expressed using a graphical modeling language, to understand, design, and evaluate CAS. As an example, an OpEMCSS model of two intelligent agents is discussed that learn rules and maximize their average reward in the prisoner's dilemma game.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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Axelrod, R. M., 1984. The Evolution of Cooperation, New York, NY: Basic Books, Inc.
 
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Clymer, J. R., 1990. Systems Analysis Using Simulation and Markov Models, Englewood Cliffs, NJ: Prentice-Hall Inc.
 
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Clymer, J. R., P. D. Corey, and J. Gardner, 1992. Discrete Event Fuzzy Airport Control, IEEE Transactions on Systems, Man, and Cybernetics, 22 (2): 343--351.
 
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Clymer, J. R., D. J. Cheng, and D. Hernandez, 1992. Induction of Decision Making Rules for Context Sensitive Systems, Simulation, San Diego, CA: The Society of Computer Simulation International, 59 (3): 198--206.
 
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Clymer, J. R., P. D. Corey, and H. Bandukwala, 1994. Induction of Classification Rules From Noisy Sonar Features, Simulation, San Diego, CA: The Society of Computer Simulation International, 62 (4): 256--267.
 
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Clymer, J. R., 1995. Induction of Fuzzy Rules for Air Traffic Control, IN Proceedings-1995 IEEE International Conference on Systems, Man, and Cybernetics, Vancouver, British Columbia, Canada, October 22--25, 1995, pages 1495--1502.
 
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Clymer, J. R., 1997. Expansionist/Context-Sensitive Methodology: Engineering of Complex Adaptive Systems, IEEE Transactions on Aerospace and Electronic Systems, 33 (2): 686--695.
 
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Clymer, J. R., 1999. Simulation-Based Engineering of Complex Adaptive Systems, Simulation, San Diego, CA: The Society of Computer Simulation International, 72 (4): 250--260.
 
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Clymer, J. R., 2001. Simulation-Based Engineering Of Complex Systems, Placentia, CA: John R. Clymer & Associates.
 
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