Modelo Imunológico de Diagnóstico
Resumo
As abordagens tradicionais de diagnóstico (diagnóstico baseado em modelos, abdutivo, e baseado em restrições) operam sobre padrões pré-definidos normais e anormais de funcionamento do sistema. Elas não tratam problemas onde tal classificação pode ser variável. Neste artigo propõe-se um novo modelo de diagnóstico inspirado no sistema imunológico natural buscando-se tal flexibilidade. Considerou-se para a elaboração desse modelo a Teoria Imunológica do Perigo, proposta por Matzinger. Tal abordagem traz como vantagem a possibilidade de um sistema computacional discriminar situações anormais de perigo daquelas que embora sejam anormais, não causarão danos ao sistema e seu ambiente.Referências
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.
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.
Publicado
20/07/2009
Como Citar
WEBBER, Carine G.; VIGANÓ, Evânia; POSSAMAI, Rodrigo.
Modelo Imunológico de Diagnóstico. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 7. , 2009, Bento Gonçalves/RS.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2009
.
p. 342-351.
ISSN 2763-9061.
