Skip to main content
SpringerLink
Log in

Interacting Agents in a Network for in silico Modeling of Nature-Inspired Smart Systems

  • Chapter
Computational Intelligence for Agent-based Systems

Part of the book series: Studies in Computational Intelligence ((SCI,volume 72))

  • 379 Accesses

An interacting multi-agent system in a network can model the evolution of a Nature-Inspired Smart System (NISS) exhibiting the four salient properties: (i) Collective, coordinated and efficient (ii) Self-organization and emergence (iii) Power law scaling or scale invariance under emergence (iv) Adaptive, fault tolerant and resilient against damage. We explain how these basic properties can arise among agents through random enabling, inhibiting, preferential attachment and growth of a multiagent system. The quantitative understanding of a Smart system with an arbitrary interactive topology is extremely difficult.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. B. Alberts et al., The Molecular Biology of the Cell, Garland Science, New York, 2002.

    Google Scholar 

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

    Article  Google Scholar 

  3. R. Aviv and E. Shapiro, Cellular Abstractors: Cellular computation, Nature , Vol. 419, p. 343, 2002.

    Article  Google Scholar 

  4. J.L. Ayers, et al., Neurotechnology for Biomimetical Robots, M.I.T. Press, Cambridge, Mass., 2002.

    Google Scholar 

  5. G.L. Baker and J.A. Blackburn, The Pendulum, Oxford University Press, Oxford, 2005.

    MATH  Google Scholar 

  6. A. Barabasi and R. Albert, Emergence of Scaling in Random Networks, Scence, Vol. 286, pp. 509-512, 1999.

    MathSciNet  Google Scholar 

  7. Y. Bar-Cohen, and C. Breazeal, Biologically-Inspired Intelligent Robotics, S.P.I.E. Press, Bellingham, Washington, U.S.A, 2003.

    Google Scholar 

  8. E. Ben-Jacob et al., Smart bacterial colonies, in Physics of Biological systems: From Molecules to Species, Lecture Notes in Physics, Vol. 480, pp. 307-340, Springer Verlag, New York, 1997.

    Google Scholar 

  9. E. Ben-Naim et al., (Eds)., Complex Networks, Lecture Notes in Physics, Vol. 650, Springer Verlag, New York, 2004.

    MATH  Google Scholar 

  10. J.M. Benyus, Biomimicry, Harpes, New York, 2002.

    Google Scholar 

  11. T. Blackwell and J. Branke, Multi-swarm Optimization in Dynamic environments, Lecture Notes in Computer Science, 3005, Springer Verlag, New York, pp. 489-500, 2004.

    Google Scholar 

  12. K. Boahen, Neuromorphic microchips, Scientific American, Vol. 292, pp. 38-41, May 2005.

    Article  Google Scholar 

  13. L. Boloni, et al., Software Engineering Challenges for mutable-agent systems, Lecture Notes in Computer Science, Vol. 2940, pp. 149-166, Springer Verlag, New York, 2004.

    Google Scholar 

  14. E. Bonabeau, M. Dorigo and G. Theraulaz, Swarm Intelligence: From Natural to Artificial systems, Oxford University Press, London, U.K., 1999.

    MATH  Google Scholar 

  15. M. Boncheva et al., Millimeter scale self-assembly and its applications, Pure and Applied Chemistry, Vol. 76(5), pp. 621-630, 2003.

    Article  Google Scholar 

  16. D. Bray, Cell movements, Garland, New York, 1996.

    Google Scholar 

  17. D.R. Brooks and E.O. Wiley, Evolution as Entropy, Univerity of Chicago Press, Chicago, Ill, 1988.

    Google Scholar 

  18. S. Bullock, The invention of algorithmic biology, in Richard Hawkins, pp. 116-124, Oxford University Press, Oxford, A. Griffen and M. Ridley (Eds), 2006.

    Google Scholar 

  19. A. Bunde and S. Havlin, Fractals in Science, Springer Verlag, New York, 1994.

    MATH  Google Scholar 

  20. S. Camazine, Self-Organization in Biological Systems, Princeton University Press, Princeton, 2002.

    Google Scholar 

  21. L. Cardelli, Abstract Machines in Systems Biology, Springer Transactions on Biological Systems, Springer Verlag, New York, 2005.

    Google Scholar 

  22. G. Chaitin, Two Philosphical Applicatons of Algorithmic Information Theory, Lecture Notes in Computer Science, Vol. 2731, Springer Verlag, New York, pp. 1-10, 2003.

    Google Scholar 

  23. S. Chu et al., Parallel Ant colony Systems, Lecture Notes In Artificial Intelligence, Vol. 2871, Springer Verlag, New York, pp. 279-284, 2003.

    Google Scholar 

  24. L.N. de Castro and J.I. Timmis, Artificial Immune Systems: A New computational Intelligence Approach, Springer Verlag, New York, 2002.

    MATH  Google Scholar 

  25. M. Dorigo et al., Ant Algorithms, Lecture Notes in Computer Sience, Vol. 2463, Springer Verlag, New York, 2002.

    Google Scholar 

  26. S.N. Dorogovtsev and J.F.F. Mendes, Evolution of Networks, Oxford University Press, Oxford, 2003.

    MATH  Google Scholar 

  27. W. Ebeling and F. Schweitzer, Self-organization, Active Brownian dynamics and biological Applications, Nova Acta Leopoldina, Vol. 88, no. 332, pp. 169-188, 2003.

    MathSciNet  Google Scholar 

  28. S. Effroni, D. Harel and I. Cohen, Reactive animation: Realistic Modeling of Complex Dynamic Systems, IEEE Computer, pp. 33-46, January 2005.

    Google Scholar 

  29. M. Falcioni et al., Kolmogorov’s legacy about entropy, Chaos and Complexity, Lecture Notes in Physics, Vol. 636, Springer Verlag, New York, pp. 85-108, 2003.

    Google Scholar 

  30. N. Forbes, Imitation of Life, M.I.T. Press, Cambridge, Mass., 2004.

    Google Scholar 

  31. D. Fudenberg and J. Tirole, Game Theory, MIT Press, Cambridge, Mass., 1991.

    Google Scholar 

  32. I. Garibay, A.S. Wu and O. Garibay, Emergence of Genomic self-similarity in location independent representations, Genetic programming and evolvable Hardware,Vol. 7, pp. 55-80, 2006.

    Article  Google Scholar 

  33. D.E. Goldberg, 1989, Genetic algorithms in search, optimisation and machine learning, Addison Wesley, Reading, Mass. 1989.

    Google Scholar 

  34. L. Goncharova, et al., Biomolecular Immunocomputing, Lecture Notes in Computer Science, Vol. 2787, pp. 102-110, Springer Verlag, New York, 2003.

    Google Scholar 

  35. I. Gorton, Evaluating agent Architectures: Cougaar, Aglets and AAA, Lecture Notes in Computer Science, Vol. 2940, Springer Verlag, New York, pp. 264-274, 2004.

    Google Scholar 

  36. I. Graham and T. Duke, The logical repertoire of ligand-binding proteins, Physical Biology, Vol. 2, pp. 159-165, 2005.

    Article  Google Scholar 

  37. G. Grimmett, Percolation, Springer, New York, 1999.

    MATH  Google Scholar 

  38. S. Guerin and D. Kunkle, Emergence of Constraint in Self-organizing Systems, Nonlinear dynamics, Psychology and Life Sciences, Vol. 8, No. 2, pp. 131-146, 2004.

    Google Scholar 

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

  40. R.C. Hilborn, Chaos and Nonlinear Dynamics, Oxford University Press, Oxford, 2003.

    Google Scholar 

  41. J. Horgan, From complexity to perplexity, Scientific American, pp. 74-79, June 1995.

    Google Scholar 

  42. A.A. Hopgood, The State of Artificial Intelligence, pp. 3-77, Advances in Computers, Vol. 65, Elsevier, New York, 2005.

    Google Scholar 

  43. F.C. Hoppensteadt and E.M. Izhikevich, Weakly Connected Neural Networks, Springer, New York, 1997.

    Google Scholar 

  44. A. Ilachinski, Cellular Automata, World Scientific, Singapore, 2001.

    MATH  Google Scholar 

  45. M. Jacobson, Developmental Neurobiology, Plenum Press, New York, 1992.

    Google Scholar 

  46. S.A. Kauffman, The origins of Order, Oxford University Press, Oxford, 1993.

    Google Scholar 

  47. J.W. Keele and J.E. Wray, Software Agents in molecular computational Biology, Briefings in Bioinformatics, Vol. 6, no. 5, pp. 370-379, December 2005.

    Article  Google Scholar 

  48. J. Kennedy and R.C. Eberhart, Swarm Intelligence, Morgan Kauffman, London, 2001.

    Google Scholar 

  49. J. Kennedy, Swarm Intelligence, in Handbook of Nature-Inspired & Innovative Computing, Ed: A. Zomaya, Springer Verlag, New York, pp. 187-221, 2006.

    Google Scholar 

  50. M. Klusch and A. Gerber, Dynamic coalition formation among Rational Agents, IEEE Intelligent systems, pp. 42-47, May/Jun 2002.

    Google Scholar 

  51. J.R. Koza, Genetic programming III, Morgan Kaufmann, San Francisco, 1999.

    MATH  Google Scholar 

  52. E.V. Krishnamurthy, Parallel Processing, Addison Wesley, Reading, Mass., 1989.

    MATH  Google Scholar 

  53. E.V. Krishnamurthy et al., Multiset Rule-based Programmming Paradigm for Soft Computing in Complex Systems, in Handbook of Nature-Inspired & Innovative Computing, Ed: A. Zomaya, Springer Verlag, New York, pp. 77-109, 2006.

    Chapter  Google Scholar 

  54. E.V. Krishnamurthy and V.K. Murthy, Distributed Agent Paradigm for soft and hard computation, Journal of Netwok and Computer Applications, Vol. 29, pp. 124-146, 2006.

    Article  Google Scholar 

  55. S. Kumar and P.J. Bentley, Biologically inspired Evolutionary Development, Lecture Notes in Computer science, Vol. 2606, pp. 57-68, Springer Verlag, New York, 2003.

    Google Scholar 

  56. D.A. Lauffenburger and J.L. Linderman, Receptors, Oxford University Press, Oxford, 1993.

    Google Scholar 

  57. J. Lee et al., Principles of locomotion for simple-shapedcells, Nature, Vol. 362, pp. 467-471, 1993.

    Article  Google Scholar 

  58. C. Lucena, C et al., Software Engineering for Multi-agent Systems, Lecture Notes in Computer Science, Vol. 2940, Springer Verlag, New York, 2004.

    MATH  Google Scholar 

  59. P.K. Maini and H.G. Othmar (Eds.), Mathematical models for biolgical patternformation, Springer Verlag, New York, 2001.

    Google Scholar 

  60. S.C. Manrubia et al., Emergence of dynamical order, World Scientific, Singapore, 2004.

    MATH  Google Scholar 

  61. G. Marcus, The birth of mind, Perseus Group, New York, 2004.

    Google Scholar 

  62. A.D. Mc Cullogh and G. Huber, Integrative Biological modelling in silico, pp. 4-19, in silico biological processes, no. 247, Novatis Foundation symposium, G. Bock and A. Goode, Eds., John Wiley and Sons, Chichester, U.K., 2002.

    Google Scholar 

  63. B. Meyer, Applying design by contracts, IEEE Computer Vol. 25(10), 40-52, 1992.

    Google Scholar 

  64. Z. Michalewicz and D.B. Fogel, 2000, How to Solve it: Modern Heuristics, Springer Verlag, New York, 2000.

    MATH  Google Scholar 

  65. A. Minelli, The Development of Animal Forms, Cambridge University Press, Cambridge, U.K., 2003.

    Google Scholar 

  66. N.A.M. Monk et al., Spatio-temporal patterning in models of juxtacrine intercellular signalling with feedback, in Mathematical Models for Biological Pattern Formation, Ed: P.K. Maini and H.G. Othmar, Springer Verlag, New York, 2001.

    Google Scholar 

  67. F.C. Moon, Chaotic and Fractal Dynamics, John Wiley, New York, 1999.

    Google Scholar 

  68. H.J. Morovitz, The emergence of Everything, Oxford University Press, Oxford, 2002.

    Google Scholar 

  69. V.K. Murthy and E.V. Krishnamurthy, Probabilistic Parallel Programming based on multiset transformation, Future generation Computer systems Vol. 11, pp. 283-293, 1995.

    Article  Google Scholar 

  70. V.K. Murthy and E.V. Krishnamurthy, Contextual information Management using Contract-baased workflow, Proc. ACM Computing Frontiers, CF’05, Iscia, Italy, 2005.

    Google Scholar 

  71. M.E.J. Newman, The Structure and Function of complex Networks, Santa Fe Institute, 2004.

    Google Scholar 

  72. H. Nishimura et al., Neural chaos scheme for perceptual conflicts, Lecture Notes In Artificial Intelligence, Vol. 2773, Springer Verlag, New York, pp. 170-196, 2003.

    Google Scholar 

  73. M.J. Osborne and A. Rubinstein, A course on Game Theory, MIT Press, Cambridge, Mass., 1994.

    Google Scholar 

  74. K.M. Pacino, Biomimicry of bacterial foraging for distributed optimisation and control IEEE Control System Magazine, Vol. 22(3), pp. 52-68, 2002.

    Article  Google Scholar 

  75. R.H. Petrucci, W.S. Harwood, and F.G. Herring, General chemistry, Prentice Hall, NJ, 2002.

    Google Scholar 

  76. A. Pikovsky et al., Synchronization, Cambridge University Press, Cambridge, 2003.

    MATH  Google Scholar 

  77. J.W. Pinney et al., Petri net representations in systems biology, Biochemical Society Transactions, Vol. 31, Part 6, 2003.

    Google Scholar 

  78. I. Prigogine, From being to becoming, W.H. Freeman and Co, San Francisco, 1980.

    Google Scholar 

  79. R. Ransom, Computers and Embryos, John Wiley, New York, 1981.

    Google Scholar 

  80. M.R. Rose and G.V. Lauder, Adaptation, Academic Press, New York, 1996.

    Google Scholar 

  81. P.V. Sankar and E.V. Krishnamurthy, On the compactness of subsets of digital pictures, Computer Graphics and image Processing, Vol. 8, pp. 136-143, 1978.

    Article  Google Scholar 

  82. F. Schweitzer, Brownian Agents and Particles, Springer Verlag, Berlin, 2002.

    Google Scholar 

  83. G.D.M. Serugendo, et al., Self Organization: Paradigms and Applications, Lecture Notes in Artificial Intelligence, Vol. 2977, Springer Verlag, New York, pp. 1-19, 2004.

    Google Scholar 

  84. G.D. Serugendo, Engineering Emergent Behaviour: A Vision, Lecture Notes in Artificial Intelligence, Vol. 2927, Springer Verlag, New York, pp. 1-7, 2003.

    Google Scholar 

  85. G.D.M. Serugendo, M.P. Gleizes, and A. Karageorgos, Self-organization and Emergence in MAS: An overview, Informatica, Vol. 30, pp. 45-54, 2006.

    MATH  Google Scholar 

  86. E. Shakshuki and Y. Jun, Multi-agent development toolkits: An Evaluation, Lecture Notes in Artficial intelligence, 3029, Springer Verlag, New York, pp. 209-218, 2004.

    Google Scholar 

  87. J.S. Sichman et al., Multi-agent-based simulation II, Lecture Notes In Artificial Intelligence, Vol. 2581, Springer Verlag, New York, 2003.

    Book  MATH  Google Scholar 

  88. R.C. Smith, Smart Material Systems, SIAM, Philadelphia, 2005.

    MATH  Google Scholar 

  89. C. Song et al., Complex Networks are Self-similar, Preprint, Levich Institute, New York, 2004.

    Google Scholar 

  90. S. Stepney et al., Artificial Immune System and the grand challenges for nonclassical computation, Lecture notes in Computer Science, Vol. 2787, Springer Verlag, New York, pp. 204-216, 2003.

    Google Scholar 

  91. B.J. Stith, Use of animation in teaching cell biology, Cell. Biology Education, Vol. 3(3), Fall, pp. 181-188, 2004.

    Article  Google Scholar 

  92. D. Strauss, On a general class of models for interaction, SIAM Review, Vol. 28, pp. 513-527, 1986.

    Article  MATH  MathSciNet  Google Scholar 

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

    Google Scholar 

  94. T. Strossel, On the crawling of animal cells, Science, Vol. 260, 1086-1094, 1993.

    Article  Google Scholar 

  95. Y. Suzuki et al., Artificial Life applications of a class of P systems: Abstract rewriting systems on Multisets, Lecture notes in Computer Science, Vol. 2235, pp. 299-346, Springer Verlag, New York, 2001.

    Google Scholar 

  96. A. Szarowicz et al., The application of AI to automatically generated animation, Lecture Notes in Artificial Intelligence, Vol. 2266, pp. 487-494, Springer Verlag, New York, 2001.

    Google Scholar 

  97. A. Szarowicz et al., Combining intelligent agents and animation, see web., 2006.

    Google Scholar 

  98. G. Szirtes et al., Emergence of Scale-free Properties in Hebbian Networks, International Journal of Neural Systems, 2001, preperint.

    Google Scholar 

  99. H. Tianfield, A Study on the Multi-agent Approach to Large Complex Systems, Lecture Notes in Artificial Intelligence, Vol. 2773, pp. 438-444, Springer Verlag, New York, 2003.

    Google Scholar 

  100. S. Torquato, Random Heterogeneous Materials, Springer, New York, 2002.

    MATH  Google Scholar 

  101. A.M. Turing, The Chemical Basis for Morphogenesis, Phil. Trans. Roy. Soc. London, Vol. 237, pp. 37-79, 1952.

    Article  Google Scholar 

  102. D. Watts, Small Worlds, Princeton University Press, Princeton, 1999.

    Google Scholar 

  103. Waves World, An agent- based approach to animation, Xenia. media.mit.edu/ awave/Ph.D.Thesis/ch4.,2004.

    Google Scholar 

  104. P. Weirich, Equilibrium and Rationality, Cambridge University Press, Cambridge, U.K., 1998.

    Google Scholar 

  105. T.F. Weiss, Cellular Biophysics, Vols. 1 and 2, MIT Press, 1996.

    Google Scholar 

  106. J. Werfel, Y. Bar-Yam, and R. Nagpal, Construction by robot swarms using extended stigmergy, Technical Report, AI Memo AIM-2005-011, MIT, Computer Science and AI Lab, 2005.

    Google Scholar 

  107. S. Wolfram, A New kind of Science, Wolfram Media Inc., Champaign, Ill, 2002.

    MATH  Google Scholar 

  108. J.C. Wooley and H.S. Lin, (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 

  109. M. Woolridge, Introduction to Multi-Agent systems, John Wiley, New York, 2002.

    Google Scholar 

  110. M. Zak et al., From Instability to Intelligence, Springer Verlag, New York, 1997.

    MATH  Google Scholar 

  111. G.M. Zaslavsky, Chaotic Dynamics and the origin of Statistical Laws, Phys- ics Today, Vol. 52(3), pp. 39-45, 1999.

    Article  Google Scholar 

  112. Zhang and C. Zhang, Agent-Based Hybrid Intelligent Systems, Lecture Notes in Artificial Intelligence, Vol. 2938, Springer Verlag, New York, 2004.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Business Information Technology, R.M.I.T University, 3000, Melbourne, Australia

    V. K. Murthy

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

    E. V. Krishnamurthy

Authors
  1. V. K. Murthy
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. E. V. Krishnamurthy
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. The Hong Kong Polytechnic University, Hong Kong, China

    Raymond S. T. Lee

  2. Universita di Salerno via Ponte don Melillo, 84084, Fisciano, Italy

    Vincenzo Loia

Rights and permissions

Reprints and permissions

Copyright information

© 2007 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Murthy, V.K., Krishnamurthy, E.V. (2007). Interacting Agents in a Network for in silico Modeling of Nature-Inspired Smart Systems. In: Lee, R.S.T., Loia, V. (eds) Computational Intelligence for Agent-based Systems. Studies in Computational Intelligence, vol 72. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73177-1_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-73177-1_7

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-73175-7

  • Online ISBN: 978-3-540-73177-1

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation