Abstract
The vertebrate immune system is a complex distributed system capable of learning to tolerate the organisms’ tissues, to assimilate a diverse commensal microflora, and to mount specific responses to invading pathogens. These intricate functions are performed almost flawlessly by a self-organised collective of cells. The robust mechanisms of distributed control in the immune system could potentially be deployed to design multiagent systems. However, the essence of the immune system is clonal expansion by cell proliferation, which is difficult to envisage in most artificial multiagent systems. In this paper, we investigate under which conditions proliferation can be approximated by recruitment in fixed-sized agent populations. Our study is the first step towards bringing many of the desirable properties of the adaptive immune system to systems made of agents which are incapable of self-replication. We adopt the crossregulation model of the adaptive immune system. We develop ordinary differential equation models of proliferation-based and recruitment-based systems, and we compare the predictions of these analytical models with results obtained by a stochastic simulation. Our results define the operational parameter regime wherein growth by recruitment retains all the properties a cell proliferation model. We conclude that rich immunological behaviour can be fully recapitulated in sufficiently large multiagent systems based on growth by recruitment.
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Tarapore, D., Christensen, A.L., Lima, P.U., Carneiro, J. (2012). Clonal Expansion without Self-replicating Entities. In: Coello Coello, C.A., Greensmith, J., Krasnogor, N., Liò, P., Nicosia, G., Pavone, M. (eds) Artificial Immune Systems. ICARIS 2012. Lecture Notes in Computer Science, vol 7597. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33757-4_15
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DOI: https://doi.org/10.1007/978-3-642-33757-4_15
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