Abstract
The Immune System (IS) represents the defence mechanism of higher level organisms to micro organismic threats. It is a genuinely distributed system provided with mechanisms of adaptation to unknown threats by means of the cooperation of heterogenous autonomous entities. Features of the overall systems, such as learning capabilities, possibility to tackle unknown threats in any part of the body, are a consequence of these interactions. This paper describes how a Multi-Agent approach, and more precisely the Situated Cellular Agents (SCA) model, can be suitably applied to represent specific elements and mechanisms of the IS. After a brief description of the composing parts and the internal mechanisms of the IS, the SCA model will be introduced and exploited to represent them.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Bandini, S.: Hyper-cellular automata for the simulation of complex biological systems: a model for the immune system. In: Kirschner, D. (ed.) Special Issue on Advance in Mathematical Modeling of Biological Processes, vol. 3 (1996)
Bandini, S., Manzoni, S., Simone, C.: Dealing with Space in Multi-Agent Systems: a model for Situated MAS. In: Proceedings of the First International Joint Conference on Autonomous Agents and Multi–Agent Sytems (AAMAS 2002), pp. 1183–1190. ACM press, New York (2002)
Bandini, S., Mauri, G., Vizzari, G.: Supporting Action–at–a–distance in Situated Cellular Agents. In: Fundamenta Informaticae. IOS press, Amsterdam (2005) (in press)
Celada, F., Seiden, P.: A computer model of cellular interactions in the immune system. Immunology Today 13(2), 56–62 (1992)
Ferber, J.: Multi-Agents Systems. Addison Wesley, Reading (1999)
Kephart, J.O., Sorkin, G.B., Swimmer, M., White, S.R.: Blueprint for a Computer Immune System. In: Dasgupta, D. (ed.) Artificial Immune Systems and Their Applications, pp. 221–241. Springer, Heidelberg (1999)
Kleinstein, S.H., Seiden, P.E.: Simulating the Immune System. IEEE Computing in Science and Engineering 2(4), 69–77 (2000)
Krishna Kumar, K., Neidhoefer, J.: Immunized Adaptive Critics for an Autonomous Aircraft Control Application. In: Dasgupta, D. (ed.) Artificial Immune Systems and Their Applications, pp. 242–261. Springer, Heidelberg (1999)
Moss, S., Davidsson, P. (eds.): MABS 2000. LNCS (LNAI), vol. 1979, pp. 97–107. Springer, Heidelberg (2001)
Puzone, R., Kohler, B., Seiden, P., Celada, F.: IMMSIM, a flexible model for in machina experiments on immune system responses. Future Generation Computer Systems 18(7), 961–972 (2002)
Přiikrylová, D., Jílek, M., Waniewski, J.: Mathematical Modeling of the Immune Response. CRC Press, Boca Raton (1992)
Repast website (2003), http://repast.sourceforge.net
Roitt, I.: Essential Immunology. Blackwell, Malden (1994)
Wolfram, S.: Theory and Applications of Cellular Automata. World Press (1986)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Bandini, S., Celada, F., Manzoni, S., Puzone, R., Vizzari, G. (2006). Modelling the Immune System with Situated Agents. In: Apolloni, B., Marinaro, M., Nicosia, G., Tagliaferri, R. (eds) Neural Nets. WIRN NAIS 2005 2005. Lecture Notes in Computer Science, vol 3931. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11731177_31
Download citation
DOI: https://doi.org/10.1007/11731177_31
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-33183-4
Online ISBN: 978-3-540-33184-1
eBook Packages: Computer ScienceComputer Science (R0)