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Multiset of Agents in a Network for Simulation of Complex Systems

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Recent Advances in Nonlinear Dynamics and Synchronization

Part of the book series: Studies in Computational Intelligence ((SCI,volume 254))

Abstract

A Complex System (CS) exhibits the four salient properties: (i) Collective, coordinated and efficient interaction among its components (ii) Self-organization and emergence (iii) Power law scaling under emergence (iv) Adaptation, fault tolerance and resilience against damage of its components. We describe briefly, three interrelated mathematical models that enable us to understand these properties: Fractal and percolation model, Stochastic / Chaotic (nonlinear) dynamical model and Topological (network) or graph model. These models have been very well studied in recent years and are closely related to the properties such as: self-similarity, scale-free, resilience, self-organization and emergence. We explain how these properties of CS can be simulated using the multi-set of agents-based paradigm (MAP) through random enabling, inhibiting, preferential attachment and growth of the multiagent network. We discuss these aspects from the point of view of geometric parameters-Lyapunov exponents, strange attractors, metric entropy, and topological indices-Cluster coefficient, Average degree distribution and the correlation length of the interacting network.

We also describe the advantages of agent-based modelling, simulation and animation. These are illustrated by a few examples in swarm dynamics- ant colony, bacterial colonies, human-animal trails, and graph-growth. We briefly consider the engineering of CS, the role of scales, and the limitations arising from quantum mechanics. A brief summary of currently available agent-tool kits is provided. Further developments of agent technology will be of great value to model, simulate and animate, many phenomena in Systems biology-cellular dynamics, cell motility, growth and development biology (Morphogenesis), and can provide for improved capability in complex systems modelling.

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References

  1. Adamic, L.A., et al.: Local search in Unstructured Networks. In: Born holdt, S., Schuster, H.G. (eds.) Handbook of Graphs and Networks, pp. 295–317. Wiley-VCH, New York (2003)

    Google Scholar 

  2. Albert, V.A.: Parsimony, Phylogeny and Genomics. Oxford University Press, Oxford (2005)

    MATH  Google Scholar 

  3. Alberts, B., et al.: The Molecular Biology of the Cell. Garland Science, New York (2002)

    Google Scholar 

  4. Atilgan, A.R., et al.: A Small -world Communication of Residues and Significance in Protein Dynamics. Biophysical Journal 86(1), 85–91 (2004)

    Article  Google Scholar 

  5. Aviv, R., Shapiro, E.: Cellular Abstractors: Cellular computation. Nature 419, 343 (2002)

    Article  Google Scholar 

  6. Baker, G.L., Blackburn, J.A.: The Pendulum. Oxford University Press, Oxford (2005)

    MATH  Google Scholar 

  7. Barabasi, A., et al.: Emergence of Scaling in Random Networks. Science 286, 509–512 (1999)

    Article  MathSciNet  Google Scholar 

  8. Bar-Cohen, Y., Breazeal, C.: Biologically-Inspired Intelligent Robotics. S.P.I.E.Press, Bellingham (2003)

    Google Scholar 

  9. Bar-Yam, Y.: Dynamics of Complex Systems. Addison Wesley, Reading (1997)

    MATH  Google Scholar 

  10. Belew, R.K., Forrest, S.: Learning and Programming in classifier Systems. Machine Learning 3, 193–223 (1988)

    Google Scholar 

  11. Belloquid, A., Delitala, M.: Mathematical Modelling of Complex Biological Systems. Birkhauser, Boston (2006)

    Google Scholar 

  12. Ben-Jacob, E., et al.: Smart bacterial colonies in Physics of Biological systems: From Molecules to Species. Lecture Notes in Physics, vol. 480, pp. 307–340. Springer, New York (1997)

    Google Scholar 

  13. Ben-Naim, E., et al. (eds.): Complex Networks. Lecture Notes in Physics, vol. 650. Springer, New York (2004)

    MATH  Google Scholar 

  14. Bininda-Emonds, O.R.P.: Phyogenetic Super trees: Combining Information to reveal the tree of life. Kluwer Academic Press, Dordrecht (2004)

    Google Scholar 

  15. Blackwell, T., Branke, J.: Multi-swarm Optimization in Dynamic Environments. In: Raidl, G.R., Cagnoni, S., Branke, J., Corne, D.W., Drechsler, R., Jin, Y., Johnson, C.G., Machado, P., Marchiori, E., Rothlauf, F., Smith, G.D., Squillero, G. (eds.) EvoWorkshops 2004. LNCS, vol. 3005, pp. 489–500. Springer, Heidelberg (2004)

    Google Scholar 

  16. Boahen, K.: Neuromorphic microchips. Scientific American 292, 38–41 (2005)

    Article  Google Scholar 

  17. Booker, L.K., et al.: Classifier systems and Genetic Algorithms. Artificial Intelligence 40, 235–282 (1989)

    Article  Google Scholar 

  18. Bonabeau, E., et al.: Swarm Intelligence: From Natural to Artificial Systems. Oxford University Press, London (1999)

    MATH  Google Scholar 

  19. Born holdt, S., Schuster, G.H.: Handbook of Graphs and Networks. Wiley-VCH, New York (2003)

    Google Scholar 

  20. Bunde, A., Havlin, S.: Fractals in Science. Springer, New York (1994)

    MATH  Google Scholar 

  21. Camazine, S.: Self-Organization in Biological Systems. Princeton University Press, Princeton (2002)

    Google Scholar 

  22. Cannings, C., Penman, D.D.: Models of random Graphs and their Applications. In: Rao, C.R. (ed.) Handbook of Statistics, vol. 21, pp. 51–91. North Holland, Amsterdam (2003)

    Google Scholar 

  23. Cardelli, L.: Abstract Machines in Systems Biology. Springer Transactions on Biological Systems (2005)

    Google Scholar 

  24. Carr, B., Rees, M.: The anthropic principle and the structure of the physical world. Nature 278, 605–612 (1979)

    Article  Google Scholar 

  25. Chaitin, G.: Two Philosophical Applications of Algorithmic Information Theory. In: Calude, C.S., Dinneen, M.J., Vajnovszki, V. (eds.) DMTCS 2003. LNCS, vol. 2731, pp. 1–10. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  26. Chan, K.S., Tong, H.: Chaos: A statistical Perspective. Springer, New York (2002)

    Google Scholar 

  27. Chu, S., et al.: Parallel Ant colony Systems. In: Zhong, N., Raś, Z.W., Tsumoto, S., Suzuki, E. (eds.) ISMIS 2003. LNCS (LNAI), vol. 2871, pp. 279–284. Springer, Heidelberg (2003)

    Google Scholar 

  28. Chung, F., Lu, L.: Complex Graphs and Networks, American Mathematical Society. In: CBMS, Providence, Rhode Island, vol. 107 (2006)

    Google Scholar 

  29. Coello, C.A.C., et al.: Evolutionary algorithm for Solving Multi-objective Problem. Kluwer, New York (2002)

    Google Scholar 

  30. Crutchfield, J.P., Schuster, P.: Evolutionary Dynamics. Oxford University Press, Oxford (2003)

    MATH  Google Scholar 

  31. de Castro, L.N., Timmis, J.I.: Artificial Immune Systems: A New computational Intelligence Approach. Springer, New York (2002)

    MATH  Google Scholar 

  32. Dembski, W.A.: Intelligent Design. InterVarsity Press, Downers Grove, Ill (1999)

    Google Scholar 

  33. Deneubourg, J.L., et al.: Optimality of communication in self-organized behaviour. In: Hemelrijk, C.K. (ed.) Self -Organization and Evolution of Social Systems, ch.2, pp. 25–35. Cambridge University Press, Cambridge (2005)

    Google Scholar 

  34. Deutsch, A., et al.: Mathematical Modeling of Biological Systems, vol. 1 and 2. Birkhauser, Boston (2007)

    MATH  Google Scholar 

  35. Dorigo, M., et al.: Ant Algorithms 2002. LNCS, vol. 2463. Springer, Heidelberg (2002)

    Book  MATH  Google Scholar 

  36. Dorigo, M., Stutzle, T.: Ant Colony optimization. M.I.T. Press, Cambridge (2004)

    MATH  Google Scholar 

  37. Dorigo, M., et al.: Swarm-Bot: design and implementation of colonies of self-assembling robots. In: Yen, G., Fogel, D.B. (eds.) Computational Intelligence, pp. 103–136. IEEE Press, New York (2006)

    Google Scholar 

  38. Dorogovtsev, S.N., Mendes, J.F.F.: Evolution of Networks. Oxford University Press, Oxford (2003)

    MATH  Google Scholar 

  39. Doucet, A., Gordon, N., Krishnamurthy, V.: Particle Filters for State Estimation of Jump Markov Linear Systems. IEEE Trans. Signal Processing 49, 613–624 (2001)

    Article  Google Scholar 

  40. Ebeling, W., Schweitzer, F.: Self-organization, Active Brownian dynamics and biological Applications. Nova Acta Leopoldina 88(332), 169–188 (2003)

    MathSciNet  Google Scholar 

  41. Edwards, S.J.: Swarming on the Battlefield, National Defence Research Institute, RAND,U.S.A (2000)

    Google Scholar 

  42. Effroni, S., et al.: Reactive animation: Realistic Modeling of Complex Dynamic Systems. IEEE Computer, 33–46 (January 2005)

    Google Scholar 

  43. Eigen, M.: StepTowards Life. Oxford University Press, Oxford (1992)

    Google Scholar 

  44. Falcioni, M., et al.: Kolmogorov’s legacy about entropy, Chaos and Complexity. Lecture Notes in Physics, vol. 636, pp. 85–108. Springer, New York (2003)

    Google Scholar 

  45. Felenstein, J.: Inferring Phylogenesis. Sinauer associates, Sunderland (2007)

    Google Scholar 

  46. Finkelstein, A.V., Ptitsyn, O.B.: Protein Physics. Academic Press, New York (2002)

    Google Scholar 

  47. Forrest, S.: Parallelism and Programming in classifier systems. Morgan Kauffman, San Mateo (1991a)

    Google Scholar 

  48. Forrest, S.: Emergent Computation. M.I.T Press, Cambridge (1991b)

    Google Scholar 

  49. Gell-Mann, M.: The Quark and the Jaguar. W.H.Freeman, New York (1994)

    MATH  Google Scholar 

  50. Goldberg, D.E.: Genetic algorithms in search, optimisation and machine learning. Addison Wesley, Reading (1989)

    Google Scholar 

  51. Goncharova, L.B., Melnikov, Y., Tarakanov, A.O.: Biomolecular Immunocomputing. In: Timmis, J., Bentley, P.J., Hart, E. (eds.) ICARIS 2003. LNCS, vol. 2787, pp. 102–110. Springer, Heidelberg (2003)

    Google Scholar 

  52. Gorton, I., Haack, J., McGee, D.R., Cowell, A.J., Kuchar, O., Thomson, J.: Evaluating Agent Architectures: Cougaar, Aglets and AAA. In: Lucena, C., Garcia, A., Romanovsky, A., Castro, J., Alencar, P.S.C. (eds.) SELMAS 2003. LNCS, vol. 2940, pp. 264–278. Springer, Heidelberg (2004)

    Google Scholar 

  53. Graham, I., Duke, T.: The logical repertoire of ligand-binding proteins. Physical Biology 2, 159–165 (2005)

    Article  Google Scholar 

  54. Grimmett, G.: Percolation. Springer, New York (2004)

    Google Scholar 

  55. Guerin, S., Kunkle, D.: Emergence of Constraint in Self-organizing Systems, Nonlinear dynamics. Psychology and Life Sciences 8(2), 131–146 (2004)

    Google Scholar 

  56. Harel, D.: A grand challenge for computing: towards full reactive modeling of a multicellular animal, EATCS Bulletin (2003), http://www.wisdom.weizmann.ac.il/~dharel/papers/grandchallenge.doc

  57. Hilborn, R.C.: Chaos and Nonlinear Dynamics. Oxford University Press, Oxford (2003)

    Google Scholar 

  58. Hofstadter, D.: Fluid concepts and creative analysis. Basic Books inc., New York (1995)

    Google Scholar 

  59. Holland, J.H., et al.: Induction. M.I.T.Press, Cambridge (1987)

    Google Scholar 

  60. Ilachinski, A.: Cellular Automata. World Scientific, Singapore (2001)

    MATH  Google Scholar 

  61. Ishida, Y.: Immmunity Based Systems. Springer, New York (2004)

    Google Scholar 

  62. Ivancevic, V.G., Ivancevic, T.T.: Neuro-Fuzzy Associative Machinery for Comprehensive Brain and Cognition Modelling. Springer, New York (2006)

    Google Scholar 

  63. Jain, S., Krishna, S.: Graph theory and Autocatalytic networks. In: Born holdt, S., Schuster, H.G. (eds.) Handbook of Graphs and Networks, pp. 355–394. Wiley-VCH, New York (2003)

    Google Scholar 

  64. Kauffman, S.A.: The origins of Order. Oxford University Press, Oxford (1993)

    Google Scholar 

  65. Keele, J.W., Wray, J.E.: Software Agents in molecular computational Biology. Briefings in Bioinformatics 6(5), 370–379 (2005)

    Article  Google Scholar 

  66. Kennedy, J., Eberhart, R.C.: Swarm Intelligence. Morgan Kauffman, London (2001)

    Google Scholar 

  67. Kennedy, J.: Swarm Intelligence. In: Zomaya, A. (ed.) Handbook of Nature-Inspired & Innovative Computing, pp. 187–221. Springer, New York (2006)

    Chapter  Google Scholar 

  68. Koza, J.R.: Genetic programmingIII. Morgan Kaufmann, San Francisco (1999)

    Google Scholar 

  69. Krishnamurthy, E.V.: Parallel Processing. Addison Wesley, Reading (1989)

    MATH  Google Scholar 

  70. Krishnamurthy, E.V., Murthy, V.K.: Transaction Processing Systems. Prentice Hall, Sydney (1992)

    Google Scholar 

  71. Lam, L.: Nonlinear Physics for Beginners. World Scientific, Singapore (1998)

    MATH  Google Scholar 

  72. Livi, R., et al.: Kolmogorov Pathways from integrability to chaos and beyond. Lecture Notes in Physics, vol. 636, pp. 3–32. Springer, New York (2003)

    Google Scholar 

  73. Maini, P.K., Othmar, H.G. (eds.): Mathematical models for biological pattern formation. Springer, New York (2001)

    Google Scholar 

  74. Manrubia, S.C., et al.: Emergence of dynamical order. World Scientific, Singapore (2004)

    MATH  Google Scholar 

  75. Maslov, S., et al.: Specificity and stability in topology of protein networks. Science 296, 910–913 (2002)

    Article  Google Scholar 

  76. Maslov, S., et al.: Correlation profiles and motifs in complex networks. In: Bornholdt, S., Schuster, H.G. (eds.) Handbook of Graphs and Networks, pp. 168–198. Wiley-VCH, New York (2003)

    Google Scholar 

  77. McCullogh, A.D., Huber, G.: Integrative Biological modelling in silico biological processes. In: Bock, G., Goode, A. (eds.) Novatis Foundation symposium, pp. 4–19. John Wliey and Sons, Chichester (2002)

    Google Scholar 

  78. Meuleau, N., Dorigo, M.: Ant colony optimization and Stochastic Gradient Descent. Artificial Life 8, 103–121 (2002)

    Article  Google Scholar 

  79. Meyer, B.: Applying design by contracts. IEEE Computer 25(10), 40–52 (1992)

    Google Scholar 

  80. Milne, R.K.: Point processes and some related Processes. In: Rao, C.R. (ed.) Handbook of Statistics, vol. 19, pp. 599–641. North Holland, Amsterdam (2001)

    Google Scholar 

  81. Moon, F.C.: Chaotic and Fractal Dynamics. John Wiley, New York (1999)

    Google Scholar 

  82. Mosekilde, E., Mosekilde, L. (eds.): Complexity, Chaos and Biological Evolution. Plenum Press, New York (1991)

    MATH  Google Scholar 

  83. Nagel, K.: Traffic Networks. In: Bornholdt, S., Schuster, H.G. (eds.) Handbook of Graphs and Networks, pp. 248–272. Wiley-VCH, New York (2003)

    Google Scholar 

  84. Newman, M.E.J.: The Structure and Function of complex Networks, Santa Fe Institute (2004)

    Google Scholar 

  85. Noe, F., Smith, C.: Transition Networks: A unifying theme for Molecular Simulation and Computer Science. In: Deutsch, A., et al. (eds.) Mathematical Modeling of Biological Systems, ch. 11, vol. 1, pp. 121–135. Birkhauser, Boston (2007)

    Chapter  Google Scholar 

  86. Orengo, C.A., et al.: Bioinformatics. BIOS Scientific Publishers, New York (2003)

    Google Scholar 

  87. Pacino, K.M.: Biomimicry of bacterial foraging for distributed optimisation and control. IEEE Control System Magazine 22(3), 52–68 (2002)

    Article  Google Scholar 

  88. Palla, G., Vattay, G.: Spectral Transitions in Networks. New Journal of Physics 8, 306–314 (2006)

    Article  Google Scholar 

  89. Parker, L.E., et al. (eds.): Distributed Autonomous Robotic Systems. Springer, New York (2000)

    Google Scholar 

  90. Parker, T.S., Chua, L.O.: Practical Numerical algorithms for chaotic systems. Springer, New York (1989)

    MATH  Google Scholar 

  91. Petrucci, R.H., et al.: General Chemistry. Prentice-Hall, NJ (2002)

    Google Scholar 

  92. Pikovsky, A., et al.: Synchronization. Cambridge University Press, Cambridge (2003)

    MATH  Google Scholar 

  93. Pinney, J.W., et al.: Petri net representations in systems biology. Biochemical Society Transactions 31, Part 6 (2003)

    Google Scholar 

  94. Prigogine, I.: From being to becoming. W.H.Freeman and Co., San Francisco (1980)

    Google Scholar 

  95. Robert, C.P., Casella, G.: Monte Carlo Statistical Methods. Springer, Heidelberg (1999)

    MATH  Google Scholar 

  96. Scargle, J.D., Babu, G.J.: Point Processes in Astronomy. In: Rao, C.R. (ed.) Handbook of Statistics, vol. 21, pp. 795–825. North Holland, Amsterdam (2003)

    Google Scholar 

  97. Schweitzer, F.: Brownian Agents and Particles. Springer, Berlin (2002)

    Google Scholar 

  98. Serugendo, G.D.M., et al.: Self Organization: Paradigms and Applications. In: Di Marzo Serugendo, G., Karageorgos, A., Rana, O.F., Zambonelli, F. (eds.) ESOA 2003. LNCS (LNAI), vol. 2977, pp. 1–19. Springer, Heidelberg (2004)

    Google Scholar 

  99. Serugendo, G.D.M., et al.: Self-organization and Emergence in MAS: A overview. Informatica 30, 45–54 (2006)

    MATH  Google Scholar 

  100. Shakhnovich, E.: Protein folding Thermodynamics and dynamics: Where Physics, Chemistry and Biology meet. Chemical Reviews 106, 1559–1588 (2006)

    Article  Google Scholar 

  101. Shakshuki, E., Jun, Y.: Multi-agent development toolkits: An Evaluation. In: Orchard, B., Yang, C., Ali, M. (eds.) IEA/AIE 2004. LNCS (LNAI), vol. 3029, pp. 209–218. Springer, Heidelberg (2004)

    Google Scholar 

  102. Shapiro, R.: A simpler origin for life. Scientific American 296, 24–31 (2007)

    Google Scholar 

  103. Shmygelska, A., et al.: An ant colony optimization algorithm for the 2D and 3D hydrophobic polar protein folding problem. BMC Bioinformatics 6, 30–47 (2005)

    Article  Google Scholar 

  104. Shyu, S.J., Tsai, C.-Y.: Finding the longest common subsequences for multiple biological sequences by ant colony optimization. Computers and Operations Research 36, 73–91 (2009)

    Article  MATH  Google Scholar 

  105. Spall, J.C.: Introduction to Stochastic Search and Optimization. Wiley Interscience, New York (2003)

    Book  MATH  Google Scholar 

  106. Sprott, J.C.: Chaos and Time Series Analysis. Oxford University Press, Oxford (2003)

    MATH  Google Scholar 

  107. Starrus, J., et al.: Bacterial Swarming Driven by Rod shape. In: Deutsch, A., et al. (eds.) Mathematical Modeling of Biological Systems, ch. 14, vol. 1, pp. 163–174. Birkhauser, Boston (2007)

    Chapter  Google Scholar 

  108. Steinhofel, K., et al.: Relating time complexity of protein folding simulation to approximations of folding time. Computer Physics Communications 176, 465–470 (2007)

    Article  Google Scholar 

  109. Stepney, S., et al.: Artificial Immune System and the grand challenges for non- classical computation. In: Timmis, J., Bentley, P.J., Hart, E. (eds.) ICARIS 2003. LNCS, vol. 2787, pp. 204–216. Springer, Heidelberg (2003)

    Google Scholar 

  110. Stith, B.J.: Use of animation in teaching cell biology. Cell. Biology Education 3(3), 181–188 (Fall 2004)

    Article  Google Scholar 

  111. Strogatz, S.H.: Sync:The emerging science of spontaneous Order. Hyperion Press, New York (2003)

    Google Scholar 

  112. Suzuki, Y., et al.: Artificial Life applications of a class of P systems: Abstract rewriting systems on Multisets. In: Calude, C.S., Pun, G., Rozenberg, G., Salomaa, A. (eds.) Multiset Processing. LNCS, vol. 2235, pp. 299–346. Springer, Heidelberg (2001)

    Chapter  Google Scholar 

  113. Szarowicz, A., et al.: The application of AI to automatically generated animation. In: Stumptner, M., Corbett, D.R., Brooks, M. (eds.) Canadian AI 2001. LNCS (LNAI), vol. 2256, pp. 487–494. Springer, Heidelberg (2001)

    Chapter  Google Scholar 

  114. Szirtes, G., et al.: Emergence of Scale-free Properties in Hebbian Networks. International Journal of Neural Systems (2001)

    Google Scholar 

  115. Thompson, E.: Mind in Life. Harvard University Press, Cambridge (2007)

    Google Scholar 

  116. Torquato, S.: Random Heterogeneous Materials. Springer, New York (2002)

    MATH  Google Scholar 

  117. Turing, A.M.: The Chemical Basis for Morphogenesis. Phil.Trans. Roy. Soc. London 237, 37–79 (1952)

    Article  Google Scholar 

  118. Twyman, R.M.: Principles of Proteomics. BIOS Scientific Publishers, New York (2003)

    Google Scholar 

  119. Vaughan, R.T., et al.: Blazing a trail: Insect - inspired reource transportation by a robot team. In: Parker, L.E., et al. (eds.) Distributed Autonomous Robotic Systems, pp. 112–120. Springer, New York (2000)

    Google Scholar 

  120. Villani, V.: Complexity of polypeptide dynamics:Chaos, Brownian motion and elastcity in aqueous solution. Journal of Molecular Structure:THEOCHEM 621, 127–139 (2003)

    Article  Google Scholar 

  121. Watts, D.: Small Worlds. Princeton University Press, Princeton (1999)

    Google Scholar 

  122. Weiss, T.F.: Cellular Biophysics, vol. 1and 2. MIT Press, Cambridge (1996)

    Google Scholar 

  123. Werfel, J., et al.: Construction by robot swarms using extende stigmergy,Technical Report, AI Memo AIM -2005-011, MIT,C S and AI Lab (2005)

    Google Scholar 

  124. Wilkins, A.S.: The Evolution of Developmental Pathways. Sinauer Associates, Inc., Sunderland (2002)

    Google Scholar 

  125. Wolfram, S.: A New Kind of Science. Wolfram Media Inc., Champaign, Ill (2002)

    MATH  Google Scholar 

  126. Wooley, J.C., Lin, H.C. (eds.): Catalyzing inquiry at the interface of computing and biology. National Research council of the National Academies, National Academies Press, Washington, DC (2005)

    Google Scholar 

  127. Woolridge, M.: Introduction to Multi-Agent Systems. John Wiley, New York (2002)

    Google Scholar 

  128. Yang, S., et al.: Evolutionary computation in Dynamic and Uncertain Environments. Springer, New York (2007)

    Book  MATH  Google Scholar 

  129. Yang, Z.: Computational Molecular Evolution. Oxford University Press, Oxford (2007)

    Google Scholar 

  130. Yuen, D.C.K., MacDonald, B.: Theoretical considerations of Multiple particle Filters for simultaneous Localization and Map-Building. In: Negoita, M.G., Howlett, R.J., Jain, L.C. (eds.) KES 2004. LNCS (LNAI), vol. 3213, pp. 203–209. Springer, Heidelberg (2004)

    Google Scholar 

  131. Zewail, A.H.: Physical Biology-From Atoms to Medicine. Imperial College, London (2008)

    Google Scholar 

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Authors and Affiliations

  1. University of Brunei Dausssalam, Brunei

    V. K. Murthy

  2. Computer Sciences Laboratory, The Australian National University, Canberra, ACT, 0200, Australia

    V. K. Murthy & E. V. Krishnamurthy

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  1. V. K. Murthy
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  2. E. V. Krishnamurthy
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  1. Institute for Smart-Systems Technologies, Alpen-Adria Universität Klagenfurt , Universitätsstraße 65-67, 9020, Klagenfurt, Austria

    Kyandoghere Kyamakya  & Jean Chamberlain Chedjou  & 

  2. Lehrgebiet Informationstechnik, FernUniversität in Hagen , Universitätsstraße 27 - PRG, 58097, Hagen, Germany

    Wolfgang A. Halang  & Zhong Li  & 

  3. Lehrgebiet Kommunikationsnetze, FernUniversität in Hagen , Universitätsstraße 27 - PRG, 58097, Hagen, Germany

    Herwig Unger

  4. Institute for Nonlinear Science, University of California, San Diego , 9500 Gilman Drive, 92093-0402, La Jolla, CA, USA

    Nikolai F. Rulkov

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Murthy, V.K., Krishnamurthy, E.V. (2009). Multiset of Agents in a Network for Simulation of Complex Systems . In: Kyamakya, K., Halang, W.A., Unger, H., Chedjou, J.C., Rulkov, N.F., Li, Z. (eds) Recent Advances in Nonlinear Dynamics and Synchronization. Studies in Computational Intelligence, vol 254. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04227-0_6

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