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Exploiting Collaborations in the Immune System: The Future of Artificial Immune Systems

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Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 1))

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Abstract

Despite a steady increase in the application of algorithms inspired by the natural immune system to a variety of domains over the previous decade, we argue that the field of Artificial Immune Systems has yet to achieve its full potential. We suggest that two factors contribute to this; firstly, that the metaphor has been applied to insufficiently complex domains, and secondly, that isolated mechanisms that occur in the immune system have been used naïvely and out of context. We outline the properties of domains which may benefit from an immune approach and then describe a number of immune mechanisms and perspectives that are ripe for exploration from a computational perspective. In each of these mechanisms collaboration plays a key role. The concepts are illustrated using two exemplars of practical applications of selected mechanisms from the domains of machine learning and wireless sensor networks. The article suggests that exploiting the collaborations that occur between actors and signals in the immune system will lead to a new generation of engineered systems that are fit for purpose in the same way as their biological counterparts.

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References

  1. http://www.specknet.org/dev/specksim

  2. Artificial Immune Systems, Proceedings of International Conferences, Lecture Notes in Computer Science. Springer (2001-2008)

    Google Scholar 

  3. Andrews, P., Timmis, J. In: Silico Immunology, chap. Alternative Inspiration for Artificial Immune Systems: Exploiting Cohen’s Cognitive Immune Model. Springer (2007)

    Google Scholar 

  4. Andrews, P., Timmis, J. In: Silico Immunology, chap. Alternative Inspiration for Artificial Immune Systems: Exploiting Cohen’s Cognitive Model. Springer (2007)

    Google Scholar 

  5. Andrews, P.S., Timmis, J.: Inspiration for the next generation of artificial immune systems. In: Jacob, C., Pilat, M.L., Bentley, P.J., Timmis, J.I. (eds.) ICARIS 2005, vol. 3627, pp. 126–138. Springer, Heidelberg (2005)

    Google Scholar 

  6. Arvind, D., Elgaid, K., Krauss, T., Paterson, A., Stewart, R., Thayne, I.: Towards an integrated design approach to specknets. In: IEEE Int. Conf. Communications 2007, ICC 2007, pp. 3319–3324 (2007)

    Google Scholar 

  7. Bersini, H.: Why the first glass of wine is better than the seventh. In: Jacob, C., Pilat, M.L., Bentley, P.J., Timmis, J.I. (eds.) ICARIS 2005. LNCS, vol. 3627, pp. 100–111. Springer, Heidelberg (2005)

    Google Scholar 

  8. Burnet, F.: The clonal selection theory of acquired immunity. Cambridge University Press, Cambridge (1959)

    Google Scholar 

  9. Carneiro, J., Coutinho, A., Faro, J., Stewart, J.: A model of the immune network with b-t cell co-operation. i - prototypical structures and dynamics. Journal of Theoretical Biology 182, 513–529 (1996)

    Article  Google Scholar 

  10. Carneiro, J., Coutinho, A., Stewart, J.: A model of the immune network with b-t cell co-operation. ii - the simulation of ontogenisis. Journal of Theoretical Biology 182, 531–547 (1996)

    Article  Google Scholar 

  11. de Castro, L., Von Zuben, F.: Learning and optimization using the clonal selection principle. IEEE Transactions on Evolutionary Computation 6(3), 239–251 (2002)

    Article  Google Scholar 

  12. Cohen, I.: The cognitive paradigm and the immunological homunculus. Immunology Today (1992)

    Google Scholar 

  13. Cohen, I.: Tending Adam’s garden: evolving the cognitive immune self. Elsevier Academic Press, Amsterdam (2000)

    Google Scholar 

  14. Cohen, I.: Real and artifical immune systems: computing the state of the body. Nature (2007)

    Google Scholar 

  15. Coutinho, A.: Immunology at the crossroads. European Molecular Biology Organisation Reports 3(11), 1008–1011 (2002)

    Google Scholar 

  16. Davoudani, D., Hart, E.: Computing the state of specknets: An immune-inspired approach. In: To be published in Proc. Int. Symposium on Performance Evaluation of Computer and Telecommunication Systems, SPECTS 2008, Edinburgh, UK, June 16–18 (2008)

    Google Scholar 

  17. Davoudani, D., Hart, E., Paechter, B.: An immune-inspired approach to speckled computing. In: de Castro, L.N., Von Zuben, F.J., Knidel, H. (eds.) ICARIS 2007. LNCS, vol. 4628, pp. 288–299. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  18. Davoudani, D., Hart, E., Paechter, B.: Computing the state of specknets: Further analysis of an innate immune-inspired model. In: Bentley, P.J., Lee, D., Jung, S. (eds.) ICARIS 2008. LNCS, vol. 5132, pp. 95–106. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  19. De Castro, L.N., Timmis, J.: Artificial Immune Systems: A New Computational Intelligence Approach. Springer, London (2002)

    MATH  Google Scholar 

  20. Farmer, J.D., Packard, N.H., Perelson, A.S.: The immune system, adaptation and machine learning. Physica 22, 187–204 (1986)

    MathSciNet  Google Scholar 

  21. Forrest, S., Hofmeyr, S., Somayaji, A.: Computer immunology. Commun. ACM 40(10), 88–96 (1997)

    Article  Google Scholar 

  22. Forrest, S., Perelson, A., Allen, L., Cherukuri, R.: Self-nonself discrimination in a computer. In: Proceedings of the IEEE Symposium on research, security and privacy, pp. 202–212 (1994)

    Google Scholar 

  23. Freund, Y., Schapire, R.E.: A decision theoretic generalisation of on-line learning and an application to boosting. Journal of Computer and System Sciences 55(1), 119–139 (1997)

    Article  MATH  MathSciNet  Google Scholar 

  24. Friedman, J., Hastie, T., Tibshirani, R.: Additive logistic regression: a statistical view of boosting (1998), http://citeseer.ist.psu.edu/friedman98additive.html

  25. Greensmith, J., Aickelin, U., Twycross, J.: Articulation and clarification of the dendritic cell algorithm. In: Bersini, H., Carneiro, J. (eds.) ICARIS 2006. LNCS, vol. 4163, pp. 404–417. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  26. Hart, E., Bersini, H., Santos, F.: How affinity influences tolerance in an idiotypic network. J. Theor. Biology (2007)

    Google Scholar 

  27. Hart, E., Davoudani, D., McEwan, C.: Immunological inspiration for building a new generation of autonomic systems. In: Autonomics, p. 9 (2007)

    Google Scholar 

  28. Hart, E., Timmis, J.: Application areas of ais: The past, the present and the future. Applied Soft Computing 8, 191–201 (2008)

    Article  Google Scholar 

  29. Hofmeyr, S., Forrest, S.: Immunity by design. In: Proceedings of GECCO 1999, pp. 1289–1296 (1999)

    Google Scholar 

  30. Holland, J.: Adaptation in Natural and Artificial Systems. MIT Press, Cambridge (1992)

    Google Scholar 

  31. Janeway, C.A., Travers, P., Walport, M., Schlomchik, M.: Immunobiology. Garland (2001)

    Google Scholar 

  32. Jerne, N.: Towards a network theory of the immune system. Annals of Immunology (Inst. Pasteur) 125, 373–389 (1974)

    Google Scholar 

  33. Littlestone, N., Warmuth, M.K.: The weighted majority algorithm. Information and Computation 108, 212–261 (1994)

    Article  MATH  MathSciNet  Google Scholar 

  34. McEwan, C., Hart, E., Paechter, B.: Revisiting the central and peripheral immune system. In: de Castro, L.N., Von Zuben, F.J., Knidel, H. (eds.) ICARIS 2007. LNCS, vol. 4628, pp. 240–251. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  35. McEwan, C., Hart, E., Paechter, B.: Towards a model of immunological tolerance and autonomous learning. Submitted to Natural Computing (2008)

    Google Scholar 

  36. Neal, M.: Meta-stable memory in an artificial immune network. In: Timmis, J., Bentley, P.J., Hart, E. (eds.) ICARIS 2003. LNCS, vol. 2787, pp. 168–180. Springer, Heidelberg (2003)

    Google Scholar 

  37. Neal, M., Trapnell, B.J.: Go Dutch: Exploit Interactions and Environments with Artificial Immune Systems. In: Silico Immunology, Springer, Heidelberg (2007)

    Google Scholar 

  38. Neuman, Y.: The immune self, the sign and the testes. Semiotics, Evolution, Energy, Development, 85–109 (2005)

    Google Scholar 

  39. Orosz, C.: Desgin Principles for Immune System and Other Distributed Autonomous Systems, chap. An Introduction to Immuno-ecology and Immuno-informatics. Oxforf Univ.Press, Oxforf (2001)

    Google Scholar 

  40. Perelson, A.S., Weisbuch, G.: Immunology for physicists. Review of Modern Physics 69 (1997)

    Google Scholar 

  41. Schapire, R.E.: The strength of weak learnability. Machine Learning 5, 197–227 (1990), http://citeseer.ist.psu.edu/schapire90strength.html

    Google Scholar 

  42. Sompayrac, L.: How the immune system works, 3rd edn. Blackwell Publishing, Malden (2008)

    Google Scholar 

  43. Stepney, S.: In Silico Immunology, chap. Embodiment. Springer, Heidelberg (2006)

    Google Scholar 

  44. Stepney, S., Smith, R., Timmis, J., Tyrrell, A., Neal, M., Hone, A.: Conceptual frameworks for artificial immune systems. Journal of Unconventional Computing 1(3), 315–338 (2005)

    Google Scholar 

  45. Stewart, J.: Cognition without neurons: adaptation, learning and memory in the immune system. In: CC AI, pp. 7–30 (1994)

    Google Scholar 

  46. Stewart, J., Coutinho, A.: The affirmation of self: A new perspective on the immune system. Artificial Life 10, 261–276 (2004)

    Article  Google Scholar 

  47. Stibor, T., Timmis, J., Eckert, C.: On the use of hyperspheres in artificial immune systems as antibody recognition regions. In: Bersini, H., Carneiro, J. (eds.) ICARIS 2006. LNCS, vol. 4163, pp. 215–228. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  48. Timmis, J., Andrews, P., Owens, N., Clark, E.: An interdisciplinary perspective on artificial immune systems. Evolutionary Intelligence 1(1), 5–26 (2008)

    Article  Google Scholar 

  49. Timmis, J., Hart, E., Neal, M., Stepney, S., Tyrrell, A.: Immuno-engineering. In: 2nd IFIP International Conference on Biologically Inspired Collaborative Computing. IEEE Press, Los Alamitos (2008)

    Google Scholar 

  50. Timmmis, J.: Artificial immune systems - today and tomorrow. Natural Computing 6(1), 1–18 (2007)

    Article  MathSciNet  Google Scholar 

  51. Varela, F., Coutinho, A., Dupire, B., Vaz, n.: Cognitive networks: Immune, neural and otherwise. J. Theoretical Immunology (1988)

    Google Scholar 

  52. Vargas, P., de Castro, L., Michelan, R., Von Zuben, F.: An immune learning classifier network for autonomous navigation. In: Timmis, J., Bentley, P.J., Hart, E. (eds.) ICARIS 2003. LNCS, vol. 2787, pp. 69–80. Springer, Heidelberg (2003)

    Google Scholar 

  53. Vargas, P., de Castro, L., Von Zuben, F.: Mapping artificial immune systems into learning classifier systems. In: Lanzi, P.L., Stolzmann, W., Wilson, S.W. (eds.) IWLCS 2003. LNCS (LNAI), vol. 2661, pp. 163–186. Springer, Heidelberg (2003)

    Google Scholar 

  54. Voigt, D., Wirth, H., Dilger, W.: A computational model for the cognitive immune system theory based on learning classifier systems. In: de Castro, L.N., Von Zuben, F.J., Knidel, H. (eds.) ICARIS 2007. LNCS, vol. 4628, pp. 264–275. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  55. Watkins, A., Timmis, J.: Exploiting parallelism inherent in airs: an artificial immune classifier. In: Nicosia, G., Cutello, V., Bentley, P.J., Timmis, J. (eds.) ICARIS 2004. LNCS, vol. 3239, pp. 427–438. Springer, Heidelberg (2004)

    Google Scholar 

  56. Whitbrook, A., Aickelin, U.J.G.: Idiotypic immune networks in mobile robot control. IEEE Transactions on Systems, Man and Cybernetics, Part B 37(6), 1581–1598 (2007)

    Article  Google Scholar 

  57. Wong, K., Arvind, D.: Speckled computing: Disruptive technology for networked information appliances. In: Proceedings of the IEEE International Symposium on Consumer Electronics (ISCE 2004), pp. 219–223 (2004)

    Google Scholar 

  58. Wong, K., Arvind, D.K.: Specknets: New challenges for wireless communication protocols. In: Third International Conference on Information Technology and Applications. ICITA 2005, vol. 2, pp. 728–733 (2005)

    Google Scholar 

  59. Zambonelli, F., Van Dyke Parunak, H.: Signs of a revolution in computer science and software engineering. In: Petta, P., Tolksdorf, R., Zambonelli, F. (eds.) ESAW 2002. LNCS, vol. 2577, pp. 13–28. Springer, Heidelberg (2003) (revised papers)

    Chapter  Google Scholar 

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Hart, E., McEwan, C., Davoudani, D. (2009). Exploiting Collaborations in the Immune System: The Future of Artificial Immune Systems. In: Mumford, C.L., Jain, L.C. (eds) Computational Intelligence. Intelligent Systems Reference Library, vol 1. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01799-5_16

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  • DOI: https://doi.org/10.1007/978-3-642-01799-5_16

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