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Artificial Immune Systems—An Emergent Technology for Autonomous Intelligent Systems and Data Mining

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3505))

Abstract

Artificial Immune Systems (AIS) are still considered with an attitude of reserve by most practitioners in Computational Intelligence (CI), much more some of them even considering this emergent computing paradigm in an infancy stage. This work aims to prove why AIS are of interest, starting from the real-world of applications that is asking for a radical change of the information systems framework. Namely, the component-based framework must be replaced with an agent-based one, where the system complexity requires that any agent to be clearly featured by its autonomy. The AIS methods build adaptive large-scale multi-agent systems that are open to the environment, systems that are not at all fixed just after the design phase, but are real-time adaptive to unpredictable situations and malicious defects. The AIS perform the defense of a complex system against malicious defects achieving its survival strategy by extension of the concept of organization of multicellular organisms to the information systems. The main behavioral features of AIS — as self-maintenance, distributed and adaptive computational systems — are defined and described in relation to the Immune System as an information system. A comparison of AIS methodology with other Intelligent Technologies is another point of the lecture. The overview of some actual AIS applications is made using a practical engineering design strategy that views AIS as the effective software with agent-based architecture.

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Negoita, M. (2005). Artificial Immune Systems—An Emergent Technology for Autonomous Intelligent Systems and Data Mining. In: Gorodetsky, V., Liu, J., Skormin, V.A. (eds) Autonomous Intelligent Systems: Agents and Data Mining. AIS-ADM 2005. Lecture Notes in Computer Science(), vol 3505. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11492870_3

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  • DOI: https://doi.org/10.1007/11492870_3

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