Towards an emergence machine for complex systems simulations
This paper presents a simulation platform that has been realised in Java 1.1 for the study of behaviour and evolutionary processes in non-linear systems. To support the modelling of such systems, we propose the use of agent technology as high level tool to design applications. The framework enables to study emergence by exploiting distributing computing as key issues of the system behaviour. Applications developed with the platform are then simulated to adequately capture any behaviour likely to be observed, to exhibit self organised structures, and to emphasise complex processes, which are brought in action. This approach then allows the study of macroscopic collections endowed with the potential to evolve during time.
Unable to display preview. Download preview PDF.
- 1.Bak, P. Tang, C. and Wiesenfeld, K. Self-Organized criticality: An explanation of l/f Noise, Physical review Letter 59,4 (1987), 381–384.Google Scholar
- 2.Calderoni, S. and Marcenac, P. Genetic programming for automatic design of self-adaptative robots, in Proceedings of European Workshop on Genetic Programming, LNCS Springer-Verlag series (1998), 163–177.Google Scholar
- 3.Cicasele, F. and Loia, V. A Fuzzy Evolutionary Approach to the Classification Problem, Int. Journal of Intelligent and Fuzzy Systems, (1998), to be printed.Google Scholar
- 4.Ferber, J. Reactive Distributed Artificial Intelligence: Principles and Applications, in Foundations of Distributed Artificial Intelligence, N. Jennings eds, North-Holland, (1994). 5.Gilbert, N. Conte, R. Artificial Societies: the computer simulation of social life, N. Gilbert et J. Doran eds, UCL Press, (1995).Google Scholar
- 6.Lahaie, F. Grasso, J.R. Marcenac, P. and Giroux, S. Self-organized criticality as a model for eruptions dynamics: validation on Piton de la Fournaise volcano, Réunion, Comptes-Rendus de l'Académie des Sciences de Paris 323, Il.a (1996), 569–574.Google Scholar
- 7.Langton, C.B. Artificial Life, Sante Fe Institute Studies in the Sciences of Complexity, Vol. VI, Addison-Wesley publishers, (1989), 1–48.Google Scholar
- 8.Marcenac, P. Modeling MultiAgent Systems as Self-Organized Critical Systems, 31th Hawaii International Conference on System Sciences, HICSS-31, IEEE Computer Society Press, Daniel R. Dolk eds, Vol. 5, (1998), 86–95.Google Scholar
- 9.Marcenac, P. Giroux, S. GEAMAS: A Generic Architecture for Agent-Oriented Simulations of Complex Processes, International Journal of Applied Intelligence, Kluwer Academic Publishers, (1998), to be printed.Google Scholar
- 10.Marcenac, P. The multiagent approach: Complex simulations that spew realistic behaviors require independent acting variables, IEEE-Potentials 2,3 (1997), 19–23.Google Scholar
- 11.Minar, N. Burkhart, R. Langton, C. and Askenazi, M. The SWARM Simulation System: a Toolkit for Building Multi-agent Simulations, http://www.santafe.eduprojects/ swarm/, (1996).Google Scholar
- 12.Neimeyer, P. and Peck, J. Exploring Java, O'reilly eds, (1996).Google Scholar
- 13.Rumbaugh, J. Blaha, M. Premerlani, W. Eddy F. and Lorensen, W. Object-Oriented Modeling and Design, Prentice Hall Int. Eds: London, England, (1991).Google Scholar
- 14.Zukowski, J. Java AWT reference 1.1, 1074 pages, 1st edition, O'Reilly, eds, (1997).Google Scholar