FUZZY RECURRENT MAPPINGS IN MULTIAGENT SIMULATION OF POPULATION DYNAMICS SYSTEMS
DOI:
https://doi.org/10.47839/ijc.19.2.1773Keywords:
population dynamics system, multi-agent model, agent-based model, linguistic fuzzy model, recurrent model, fuzzy sets, production system.Abstract
The article deals with the problems of analyzing multi-agent models of population dynamics. The problems studied are caused by a number of uncertainties associated with variables, boundary conditions, initial states, parameter values, etc. Given problems could be found in tasks associated with cyber security of critical infrastructures (e.g. DDoS attacks, computer worms, etc.). To solve this problem, a linguistic fuzzy model has been developed, which allows describing systems of population dynamics in a more realistic way. Population dynamics is described by a set of rules, each of which involves entry and exit in the form of fuzzy sets or fuzzy functions, which are applied iteratively. The complexity of describing the processes of population dynamics systems, the presence of fuzzification and defuzzification algorithms, and the use of fuzzy sets and linguistic variables make it necessary to develop new methods for analyzing such systems. The approaches proposed in the article to the study of systems of population dynamics make it possible to apply a unified description of processes of different nature in the form of a production set of rules.References
N. Gilbert, Analyzing Tabular Data: Loglinear and Logistic Models for Social Researchers, London: UCL Press, 1993, pp. 10-14.
B. Chaib-draa, F. Dignum, “Trends in agent communication language,” Computational Intelligence, vol. 18, issue 2, pp. 89–101, 2002.
Y. Shoham, K. Leyton-Brown, Multiagent Systems – Algorithmic, Game-Theoretic, and Logical Foundations, Cambridge University Press, 2009, 483 p.
J. Hollan, E. Hutchins, D. Kirsh, “Distributed cognition: toward a new foundation for human-computer interaction research,” ACM Transactions on Computer-Human Interaction (TOCHI), vol. 7, issue 2, pp. 174-196, 2000.
P. Maes, “Agents that reduce work and information overload,” Communications of the ACM, vol. 37, pp. 31–40, 1994.
T. Rahwan, T. Michalak, M. Wooldridge, N. R. Jennings, “Towards anytime coalition structure generation in multi-agent systems with positive or negative externalities,” Artificial Intelligence, vol. 186, pp. 95–122, 2002.
M. J. Osborne, A. Rubinstein, A Course in Game Theory, Massachusetts, Cambridge: The MIT Press, 2014, 352 p.
C. H. Papadimitriou, J. N. Tsitsiklis, “The complexity of Markov decision processes,” Mathematics of Operations Research, vol. 12, issue 3, pp. 441–450, 1987.
R. Conte, R. Hegselmann, P. Terna, “Simulating social phenomena,” Lecture Notes in Economics and Mathematical Systems, vol. 456, 1997, 536 p.
F. S. Santos, N. R. S. Ortega, D. M. T. Zanetta, E. Massad, “Fuzzy Dynamical model of epidemic spreading taking into account the uncertainties in individual infectivity,” Advances in Technological Applications of Logical and Intelligent Systems, IOS Press, pp. 180-193, 2009.
D. Chumachenko, S. Yakovlev, “On Intelligent Agent-Based Simulation of Network Worms Propagation,” Proceedings of the 2019 15th International Conference on the Experience of Designing and Application of CAD Systems (CADSM), 2019, pp. 3.11-3.13.
B. Zhang, T. Zhang, Z. Yu, “DDoS detection and prevention based on artificial intelligence techniques,” Proceedings of the 2017 3rd IEEE International Conference on Computer and Communications (ICCC), Chengdu, 2017, pp. 1276-1280.
D. Chumachenko, V. Balitskii, T. Chumachenko, V. Makarova, M. Railian, “Intelligent expert system of knowledge examination of medical staff regarding infections associated with the provision of medical care,” CEUR Workshop Proceedings, vol. 2386, 2019, pp. 321-330.
I. Meniailov, K. Bazilevych, K. Fedulov, S. Goranina, D. Chumachenko, “Using the K-means method for diagnosing cancer stage using the Pandas library,” CEUR Workshop Proceedings, vol. 2386, 2019, pp. 107-116.
R. S. Sutton, A. G. Barto, Reinforcement Learning: An Introduction (Adaptive Computation and Machine Learning), The MIT Press, 2017, 322 p.
N. Gilbert, K. G. Troitzsch, Simulation for the Social Scientist, Open University Press, 2005, pp. 172–199.
L. Steels, “The Artificial Life Roots of Artificial Intelligence”, Artificial Life: an overview, the MIT Press, 2000, pp. 75–110.
D. Chumachenko, “On intelligent multiagent approach to viral hepatitis B epidemic processes simulation,” Proceedings of the 2018 IEEE 2nd International Conference on Data Stream Mining and Processing, DSMP’2018, 2018, pp. 415-419.
T.–Y. Li, J. A. Yorke, “Period three implies chaos,” Amer. Math. Monthly, vol. 82, no. 10, pp. 985–992, 1975.
R. Kempf, J. Adamy, “Regularity and chaos in recurrent fuzzy systems,” Fuzzy Sets and Systems, vol. 140, issue 2, pp. 259–284, 2003.
E. Massad, N.R.S. Ortega, L.C. de Barros, C.J. Struchiner, “Fuzzy dynamical systems in epidemic modeling,” in: Fuzzy Logic in Action: Applications in Epidemiology and Beyond. Studies in Fuzziness and Soft Computing, vol 232. Springer, Berlin, Heidelberg, 2008, pp. 181–206.
R. R. Yager, D. P. Filev, Essentials of Fuzzy Modeling and Control, N.Y.: Wiley, 1994, 408 p.
M. Mahfouf, A. J. Asbury, D. A. Linkens “Physiological modelling and fuzzy control of anaesthesia via vaporisation of isoflurane by liquid infusion,” International Journal of Simulation Systems, Science and Technology, vol. 2, issue 1, pp. 55–66, 2001.
M. A. R. B. Castanho, Membrane-Active Peptides: Methods and Results on Structure and Function, International University Line, 2010, 635 p.
A. P. Duarte, J. C. Bordado, M. T. Cidade, “Cellulose acetate reverse osmosis membranes: Optimization of preparation parameters,” Journal of Applied Polymer Science, vol. 103, issue 1, pp. 134-139, 2006.
H. Tanaka, N. R. S. Ortega, M. S. Galizia, J. B. Borges, M. B. P. Amato, “Fuzzy modeling of electrical impedance tomography images of the lungs,” Clinics, vol. 63, issue 3, pp. 363-370, 2008.
L. F. C. Nascimento, N. R. S. Ortega, “Fuzzy linguistic model for evaluating the risk of neonatal death,” Revista de Saúde Pública, vol. 36, no. 6, pp. 686–692, 2002.
R. M. Jafelice, L. C. de Barros, R. C. Bassanezi, F. Gomide, “Fuzzy modeling in symptomatic HIV virus infected population,” Bulletin of Mathematical Biology, vol. 66, issue 6, pp. 1597-1620, 2004.
L. C. Barros, R. C. Bassanezi, W. A. Lodwick, A First Course in Fuzzy Logic, Fuzzy Dynamical Systems, and Biomathematics, Springer-Verlag Berlin Heidelberg, 2017, 297 p.
V.P. Mashtalir, S.V. Yakovlev, “Point-set methods of clusterization of standard information,” Cybernetics and Systems Analysis, vol. 37, no. 3, pp. 295-307, 2001.
L. C. Barros, M. B. F. Leite, R. C. Bassanezi “The SI epidemiological models with a fuzzy transmission parameter,” International Journal of Computational Mathematical Applications, vol. 45, pp. 1619–1628, 2003.
N. Ortega, L. C. Barros, E. Massad, “Fuzzy gradual rules in epidemiology,” Kybernetes, vol. 32, issue 3, pp. 460-477, 2015.
N. R. S. Ortega, P. C. Sallum, E. Massad, “Fuzzy dynamical systems in epidemic modeling,” Kybernetes, vol. 29, issue 2, pp. 201–218, 2000.
Z. Weihong, X. Shunqing, M. Ting, “A fuzzy classifier based on Mamdani fuzzy logic system and genetic algorithm,” Proceedings of the 2010 IEEE Youth Conference on Information, Computing and Telecommunications, Beijing, 2010, pp. 198-201.
R. Kempf, J. Adamy, “Regularity and chaos in recurrent fuzzy systems,” Fuzzy Sets and Systems, vol. 140, issue 2, pp. 259–284, 2003.
R. S. Bernstein, “Multi-level simulation analysis: A methodology for planning and evaluation in public health,” The many faces of multi-level issues (Research in Multi Level Issues), vol. 1, pp. 381–386, 2002.
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