Immunological Model for Diagnosis

  • Carine G. Webber UCS
  • Evânia Viganó UCS
  • Rodrigo Possamai UCS

Abstract


Classical approaches to diagnosis (model-based, abductive and constraint -based diagnosis) depend on the definition of rules setting normal and abnormal expected behaviours. In some contexts such previous definition is not so clear. In this article we propose a new diagnosis model inspired by natural immune system, in special Matzingerís Danger Theory. This approach has the advantage of discriminating among abnormal situations, those which may cause danger to a system.

References

Aickelin, U., Cayser, S. (2002) “The danger theory and its application to AIS”. In Proceedings of the First International Conference on Artificial Immune Systems (ICARIS-2002), pp.141-148.

Burnet F.M, (1959) “The Clonal Selection Theory of Acquired Immunity”, Cambridge Univ. Press.

Console, L., Theseider Dupré, D., Torasso, P. (1989) “A theory of diagnosis for incomplete causal models”, Proceedings of the 10th IJCAI, USA, pp.1311-1317.

Dasgupta, D. (2006) Advances in Artificial Immune Systems. IEEE Computational Intelligence Magazine. pp.40-49.

De Castro, L. N. & Von Zuben, F. J. (1999) "Artificial Immune Systems: Part I – Basic Theory and Application", Tech.Report, Unicamp 01/1999, p.65.

De Castro, L. N. & Von Zuben, F. J. (2000) "Artificial Immune Systems: Part II – A Survey of Applications", Tech.Report, Unicamp 02/2000, p.65.

Farmer, J., Packard, N., Perelson, A. (1986) The immune system, adaptation and machine learning.

Physica D, 22, pp.187-204. Forrest, S., Perelson, A.S., Allen, L., Cherukuri, R.(1994) “Self-Nonself Discrimination in a Computer”, In Proceedings of the 1994 IEEE Symposium on Research in Security and Privacy, Los Alamitos, CA: IEEE Computer Society Press.

Frohlich, P. M'ora, I.A., Nejdl, W., Schroeder, M. (1997) “Diagnostic agents for distributed systems”, Proceedings of ModelAge97, Sienna, Italy.

Fukuda, T., Mori, K., Tsukiyama, M. (1993) Immune networks using genetic algorithm for adaptive production scheduling. In : Proceeding of the 15th IFAC World Congress, volume 3, pp.57-60.

Janeway, C., Travers, P., Capra, J.D., Walport, M.J. (2000) Immunobiology: The Immune System in Health and Disease, Garland Pub, pp.635.

Matzinger, P. (2002) The Danger Model: A renewed sense of self. Science, 296 (5566), pp.301-305. Maxion, R.A. (1990) Toward diagnosis as an emergent behavior in a network ecosystem. In:. Physica D 42, pp.66–84.

Reiter, R.(1987)“A theory of diagnosis from first principles”,Artificial intelligence , 32 (1), pp.57-96.

Ross, N., Teije, A., Witteveen, C. (2003) “A Protocol for Multi-Agent Diagnosis with Spatially Distributed Knowledge”, Austrália, ACM Press, pp.655-661.

Webber, C.G., Silva, J.L.T. (2008) “Self and Non-self Discrimination Agents”, In Proceedings of the 2008 ACM Symposium on Applied Computing (SAC '08), ACM, New York, 1987-1988.

Weinand, R.G. (1990) Somatic mutation, affinity maturation and antibody repertoire: A computer model. Journal of the Theoretical Biology, 143, pp.343-382.
Published
2009-07-20
WEBBER, Carine G.; VIGANÓ, Evânia; POSSAMAI, Rodrigo. Immunological Model for Diagnosis. In: NATIONAL MEETING ON ARTIFICIAL AND COMPUTATIONAL INTELLIGENCE (ENIAC), 7. , 2009, Bento Gonçalves/RS. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2009 . p. 342-351. ISSN 2763-9061.