Abstract
Researchers have used Idiotypic Networks in a myriad of applications ranging from function optimization to pattern recognition, learning and even robotics and control. Most of the reported works that have used the Idiotypic network have been simulations wherein not all entities perform in a true distributed, parallel and asynchronous manner. The concentration of an antibody within the network is always assumed to be single valued, which is easily available as a global parameter in such simulated systems. This paper describes a novel architecture and dynamics to emulate an Idiotypic network wherein antibodies within a real physical network interact at antigen-affected nodes, sense their respective global populations stigmergically and form Localized Idiotypic Networks that eventually control their respective global populations across the network. Typhon, a mobile agent platform, running at the various nodes forming the physical network, was used for the emulation. While the mobile agents acted as antibody carriers and ensured their mobility, the nodes forming the physical network formed the antigenic sites. Results, portrayed herein, show the selective rise in global populations of the set of antibodies that are more effective in neutralizing a range of antigens across the network.
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Jha, S.S., Shrivastava, K., Nair, S.B. (2013). On Emulating Real-World Distributed Intelligence Using Mobile Agent Based Localized Idiotypic Networks. In: Prasath, R., Kathirvalavakumar, T. (eds) Mining Intelligence and Knowledge Exploration. Lecture Notes in Computer Science(), vol 8284. Springer, Cham. https://doi.org/10.1007/978-3-319-03844-5_49
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DOI: https://doi.org/10.1007/978-3-319-03844-5_49
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