Representation in the (Artificial) Immune System

Article

Abstract

Much of contemporary research in Artificial Immune Systems (AIS) has partitioned into either algorithmic machine learning and optimisation, or, modelling biologically plausible dynamical systems, with little overlap between. We propose that this dichotomy is somewhat to blame for the lack of significant advancement of the field in either direction and demonstrate how a simplistic interpretation of Perelson’s shape-space formalism may have largely contributed to this dichotomy. In this paper, we motivate and derive an alternative representational abstraction. To do so we consider the validity of shape-space from both the biological and machine learning perspectives. We then take steps towards formally integrating these perspectives into a coherent computational model of notions such as life-long learning, degeneracy, constructive representations and contextual recognition—rhetoric that has long inspired work in AIS, while remaining largely devoid of operational definition.

Keywords

Artificial immune system Representation Shape-space Learning 

References

  1. 1.
    Aggarwal, C.C., Hinneburg, A., Keim, D.A.: On the surprising behavior of distance metrics in high dimensional space. Lect. Notes Comput. Sci. 1973, 420–434 (2001)CrossRefGoogle Scholar
  2. 2.
    Aharon, M., Elad, M., Bruckstein, A.: K-svd: an algorithm for designing overcomplete dictionaries for sparse representation. IEEE Trans. Signal Process. 54, 4311–4322 (2006)CrossRefGoogle Scholar
  3. 3.
    Bellman, R.: Adaptive Control Processes: A Guided Tour. Princeton University Press, Princeton (1961)MATHGoogle Scholar
  4. 4.
    Bellman, R.: Introduction to Matrix Analysis. SIAM Classics. SIAM, Philadelphia (1997)Google Scholar
  5. 5.
    Bersini, H.: Immune network and adaptive control. In: Bourgine, P., Varela, F. (eds.) Proceedings of the First European Conference on Artificial Life. MIT, Cambridge (1991)Google Scholar
  6. 6.
    Bersini, H.: Reinforcement and recruitment learning for adaptive process control. In: Proceedings of the International Fuzzy Association Conference on Artificial Intelligence in Real Time Control (1992)Google Scholar
  7. 7.
    Bersini, H.: Artificial Immune Systems and their Applications. Chapter The Endogenous Double Plasticity of the Immune Network and the Inspirationto be drawn for Engineering Artifacts. Springer, New York (1999)Google Scholar
  8. 8.
    Beyer, K., Goldstein, J., Ramakrishnan, R., Shaft, U.: When is “nearest neighbor” meaningful? Lect. Notes Comput. Sci. 1540, 217–235 (1999)CrossRefGoogle Scholar
  9. 9.
    Breiman, L.: Prediction games and arcing algorithms. Neural Comput. 11(7), 1493–1517 (1999)CrossRefGoogle Scholar
  10. 10.
    Butz, M.V.: Learning classifier systems. In: GECCO ’07: Proceedings of the 2007 GECCO Conference Companion on Genetic and evolutionary Computation, pp. 3035–3056. ACM, New York (2007)Google Scholar
  11. 11.
    Carneiro, J., Coutinho, A., Faro, J., Stewart, J.: A model of the immune network with b-t cell co-operation. i—prototypicalstructures and dynamics. J. Theor. Biol. 182, 513–529 (1996)CrossRefGoogle Scholar
  12. 12.
    Carneiro, J., Coutinho, A., Stewart, J.: A model of the immune network with b-t cell co-operation. ii—the simulation of ontogenisis. J. Theor. Biol. 182, 531–547 (1996)CrossRefGoogle Scholar
  13. 13.
    Carneiro, J., Stewart, J.: Rethinking shape space: evidence from simulated docking suggeststhat steric shape complementarity is not limiting for antibody-antigenrecognition and idiotypic interactions. J. Theor. Biol. 169, 391–402 (1994)CrossRefGoogle Scholar
  14. 14.
    Cohen, I.R.: Immune system computation and the immunological homunculus. In: Niestrasz, O., et al. (ed.) MoDELS 2006, pp. 499–512, Genova, 1–6 October 2006Google Scholar
  15. 15.
    Cohen, I.R.: Tending Adam’s Garden: Evolving the Cognitive Immune Self. Academic, London (2004)Google Scholar
  16. 16.
    Cohen, I.R., Segel, L.A.: Design Principles for the Immune System and Other Distributed AutonomousSystems. Oxford University Press, Oxford (2001)Google Scholar
  17. 17.
    De Castro, L.N., Timmis, J.: Artificial Immune Systems: A New Computational Intelligence Approach. Springer, London (2002)MATHGoogle Scholar
  18. 18.
    Detours, V., Bersini, H., Stewart, J., Varela, F.: Development of an idiotypic network in shape space. J. Theor. Biol. 170(4), 401–414 (1994)CrossRefGoogle Scholar
  19. 19.
    Douglas, R., Sejnowski, T.: Final workshop report: future challenges for the science and engineering of learning. Technical report, National Science Foundation (2007)Google Scholar
  20. 20.
    Greenbaum, J.A., et al.: Towards a consensus on datasets and evaluation metrics for developing b-cell epitope prediction tools. J. Mol. Recognit. 20, 75–82 (2007)CrossRefGoogle Scholar
  21. 21.
    Wucherpfennig, K.W., et al.: Polyspecificity of t cell and b cell receptor recognition. Semin. Immunol. 19, 216–224 (2007)CrossRefGoogle Scholar
  22. 22.
    Farmer, J.D., Packard, N.H., Perelson, A.S.: The immune system, adaptation and machine learning. Physica 22, 187–204 (1986)MathSciNetGoogle Scholar
  23. 23.
    Freitas, A., Timmis, J.: Revisiting the foundations of artificial immune systems: a problem-oriented perspective. In: ICARIS 2003: International Conference on Artificial Immune Systems (2003)Google Scholar
  24. 24.
    Freitas, A.A., Timmis, J.: Revisiting the foundations of artificial immune systems for datamining. IEEE Trans. Evol. Comput. 11-4, 521–540 (2007)CrossRefGoogle Scholar
  25. 25.
    Freund, Y., Schapire, R.: Game theory, on-line prediction and boosting. In: 9th Annual Conference on Computational Learning Theory (1996)Google Scholar
  26. 26.
    Freund, Y., Schapire, R.E.: A decision theoretic generalisation of on-line learning and an applicationto boosting. J. Comput. Syst. Sci. 55(1), 119–139 (1997)MATHCrossRefMathSciNetGoogle Scholar
  27. 27.
    Friedman, J.: Greedy function approximation: a gradient boosting machine. IMS 1999 Reitz Lecture (1999)Google Scholar
  28. 28.
    Friedman, J., Hastie, T., Tibshirani, R.: Additive logistic regression: a statistical view of boosting. Ann. Stat. 28, 337–407 (2000)MATHCrossRefMathSciNetGoogle Scholar
  29. 29.
    Friedman, J.H.: Recent advances in predictive (machine) learning. In: PHYSTAT2003 (2003)Google Scholar
  30. 30.
    Greensmith, J., Aickelin, U.: The deterministic dendritic cell algorithm. In: Proceedings of the Seventh Internation Conference on Artificial ImmuneSystems (ICARIS 2008) (2008)Google Scholar
  31. 31.
    Hart, E., Timmis, J.: Application areas of ais: the past, the present and the future. In: ICARIS 2005, LNCS 3627 (2005)Google Scholar
  32. 32.
    Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning. Springer, New York (2001)MATHGoogle Scholar
  33. 33.
    Hershberg, U., Solomon, S., Cohen, I.R.: What is the basis of the immune system’s specificity? In: Capasso, V. (ed.) Mathematical Modelling & Computing in Biology and Medicine, pp. 377–384 (2003)Google Scholar
  34. 34.
    Holland, J.: Adaptation in Natural and Artificial Systems. MIT, Cambridge (1992)Google Scholar
  35. 35.
    Cohen, E.I.R.: Real and artificial immune systems: computing the state of the body. Nat. Rev. Immunol. 7, 569–74 (2007)CrossRefGoogle Scholar
  36. 36.
    Janeway, C.A., Travers, P., Walport, M., Schlomchik, M.: Immunobiology. Garland, New York (2001)Google Scholar
  37. 37.
    Byron, F.W., Jr., Fuller, R.W.: Mathematics of Classical and Quantum Physics. Dover, New York (1992)Google Scholar
  38. 38.
    Kearns, M.J., Vazirani, U.V.: An Introduction to Computational Learning Theory. MIT, Cambridge (1994)Google Scholar
  39. 39.
    Krstulovic, S., Gribonval, R.: Mptk: matching pursuit made tractable. In: Acoustics, Speech and Signal Processing (ICASSP 2006) (2006)Google Scholar
  40. 40.
    Leon, K., Carneiro, J., Perez, R., Montero, E., Lage, A.: Natural and induced tolerance in an immune network model. J. Theor. Biol. 193, 519–534 (1998)CrossRefGoogle Scholar
  41. 41.
    Lewicki, M.S., Sejnowski, T.J.: Learning overcomplete representations. Neural Comput. 12(2), 337–365 (2000)CrossRefGoogle Scholar
  42. 42.
    Littlestone, N.: Learning quickly when irrelevant attributes abound: a new linear-threshold algorithm. Mach. Learn. 2, 285–318 (1988)Google Scholar
  43. 43.
    Littlestone, N., Warmuth, M.K.: The weighted majority algorithm. Inf. Comput. 108, 212–261 (1994)MATHCrossRefMathSciNetGoogle Scholar
  44. 44.
    Mahadevan, S.: Representation Discovery using Harmonic Analysis. Morgan and Claypool, San Rafael (2008)Google Scholar
  45. 45.
    Mallat, S.G.: Matching pursuits with time-frequency dictionaries. IEEE Trans. Signal Process. 41, 3397–3415 (1993)MATHCrossRefGoogle Scholar
  46. 46.
    Maturana, H.R., Varela, F.J.: Autopoiesis and Cognition: The Realization of the Living. Kluwer Academic, Dordrecht (1979)Google Scholar
  47. 47.
    Mendao, M., Timmis, J., Andrews, P.S., Davies, M.: The immune system in pieces: Computational lessons from degeneracyin the immune system. In: Foundations of Computational Intelligence (FOCI 2007) (2007)Google Scholar
  48. 48.
    Nanas, N., Uren, V.S., de Roeck, A.: Nootropia: a user profiling model based on a self-organising termnetwork. In: ICARIS 2004, LNCS 3239 (2004)Google Scholar
  49. 49.
    Oza, N., Russell, S.: Online bagging and boosting. In: Artificial Intelligence and Statistics 2001, pp. 105–112. Morgan Kaufmann, San Francisco (2001)Google Scholar
  50. 50.
    Perelson, A.S., Oster, G.: Theoretical studies of clonal selection: minimal antibody repertoire size and reliability of self non-self discrimination. J. Theor. Biol. 81, 645–670 (1979)CrossRefMathSciNetGoogle Scholar
  51. 51.
    Perelson, A.S., Weisbuch, G.: Immunology for physicists. Rev. Mod. Phys. 69, 1219–1267 (1997)CrossRefGoogle Scholar
  52. 52.
    Schapire, R.E.: The strength of weak learnability. Mach. Learn. 5, 197–227 (1990)Google Scholar
  53. 53.
    Shaw-Taylor, J., Cristianini, N.: Kernel Methods for Pattern Analysis. Cambridge University Press, Cambridge (2006)Google Scholar
  54. 54.
    Skurichina, M., Duin, R.P.W.: Bagging, boosting and the random subspace method for linear classifiers. Pattern Anal. Appl. 5, 121–135 (2002)MATHCrossRefMathSciNetGoogle Scholar
  55. 55.
    Stibor, T., Timmis, J., Eckert, C.: On the use of hyperspheres in artificial immune systems as antibodyrecognition regions. In: ICARIS 2006 (2006)Google Scholar
  56. 56.
    Varela, F., Coutinho, A., Dupire, B., Vaz, N.M.: Theoretical Immunology, vol. II. Chapter Cognitive Networks: Immune, Neural and Otherwise. Addison-Wesley, Reading (1988)Google Scholar
  57. 57.
    Varela, F.J., Coutinho, A.: Second generation immune networks. Immunol. Today 12(5), 159–166 (1991)Google Scholar
  58. 58.
    Vincent, P., Bengio, Y.: Kernel matching pursuit. Mach. Learn. 48, 169–191 (2001)Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2009

Authors and Affiliations

  1. 1.Napier UniversityEdinburghUK

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